BRIK Youcef

Cherdoud Ghada

Un bracelet électronique de surveillance pour les patients atteints la maladie d’Alzheimer

BENCHOHRA Narimane

ELMORIDE : Training and Business Platform

MOUSTARI Mohamed Abderaouf

Deep learning-based medical data analysis for disease prediction and classification

NADIR Cheyma

Deep Learning Approach for Multimodal Biometric Recognition System

BEGHRICHE Tawfiq

Applications of machine learning and deep learning in healthcare: Breast cancer case

Barkat Aya

Brain tumor detection using combined deep learning models

Ely Cheikh Abdellahi Aida

Developpement d'un fauteuil roulant controlé par la voix pour la langue arabe

Chbabhi Taher , Messad Ahmed

MY DAILY HEALTH: Helping system for medical diagnosis using artificial intelligence

Djerarda Imadeddine

Système de sécurité en temps réel pour les conducteurs: Détection de la somnolence et les distractions en utilisant l'intelligence artificielle

Benaamam Khalil , Sekkiou, Fayçal

Deep transfer learning-based feature extraction for breast cancer detection

Lahmar Hanine , Ziani Zahra

Early detection and classification for diabetic retinopathy by deep learning

Zouaoui Rafik , Nadir Rafik

Deep learning approach for Alzheimer diagnosis using MRI images

Arbane Mohamed

Deep learning for medical data prediction

Guenani Moussa

Embedded deep learning and Raspberry pi system for assisting visually impaired/blind people

Allal Bassem , Bouafia Fatima

Fusion bimodal pour l'identification biométrique

DEGHFEL Ebderezzak , Hrizi Abdelghani

Réalisation d'un régulateur industriel à base d'un Arduino et LabView

Beghriche Tawfiq , Bentayeb Farida

Prédiction de pathologies les plus fréquentes en utilisant deep learning

Bouguerra Oussama , Benslimane Oussama

solar radiation prediction using Machine Learning

Abdelhafid Oualid , Senouci Abdelkarim

Identification Biométrique par les veins des doigts

Mira Ouassila , Saada Sara

Détection des contours dans les images niveaux de gris 'étude comparative'

ABASSI Brahim

APPROCHE DE SÉLECTION DES DONNÉES BIOMÉDICALES DANS UN PROBLÈME DE RECONNAISSANCE DE PATHOLOGIES EN UTILISANT LES SUPPORT VECTOR MACHINES

BENABDI Mouad

IDENTIFICATION DES PERSONNES PAR LES EMPREINTES D’ARTICULATION DES DOIGTS ET LE DEEP LEARNING

MAHDI Fatma Zohra , TABI Fattoum

Caractérisation d’empreinte de l’articulation de doigt pour l’authentification des personnes

BACHIRI Mohamed , DAKHANE Mohammed

ÉVALUATION ET DETERMINATION DES VARIABLES D’ENTREE POUR UN MODELE MULTICLASSE INTELLIGENT DANS LES PROCÉDÉS DE TRAITEMENT DES EAUX PROPRES

AICHE Ishaq , KETFI Meryem

APPRENTISSAGE DES MACHINES PUISSANTES POUR LA RECONNAISSANCE DES FORMES

BENHAMIDA Assia

Repérage de Mots dans un Document Arabe Ancien en Utilisant les Caractéristiques Statistiques

DAHMANE Nassereddine

Étude et Réalisation d’un Suiveur Solaire Pour un Panneau Photovoltaïque

ZEROUGUI Sana

Repérage de Mots dans des Documents Arabes Historiques en Utilisant la Transformée de Radon

Driver fatigue remains one of the most critical factors in preventable road deaths, yet conventional systems based on Convolutional Neural Networks (CNNs) often struggle to strike a vital balance between accuracy, speed, and practical usability under diverse conditions. This paper introduces WakeUp AI, an intelligent fatigue detection system explicitly designed to bridge that gap. The core framework leverages the advanced feature extraction capabilities of a Vision Transformer (ViT), combined with an optimized Support Vector Machine (SVM) classifier, resulting in an outstanding 99.82% test accuracy on the CEW dataset. This hybrid ViT-SVM approach achieves superior feature discrimination while maintaining computational efficiency suitable for edge deployment. For real-time use, WakeUp AI integrates MediaPipe FaceMesh with a streamlined ViT model, achieving inference latency of < 100ms frame. Crucially, a continuous temporal logic module constantly monitors the driver’s eye state, activating instant audio alerts only when fatigue patterns (such as prolonged eye closure duration) are robustly detected. Unlike conventional systems limited to simple, rigid thresholding, WakeUp AI intelligently adapts to diverse environments, making it exceptionally robust. By combining state-of-the-art deep learning with real-time responsiveness, WakeUp AI offers a scalable, high-performance solution for critical safety applications.

Citation

FAYEK Maarfi , BRIK Youcef , BILAL Attallah , ,(2025-11-25), The 2nd International Workshop on Machine Learning and Deep Learning (WMLDL25),WakeUp AI – Fatigue Detection System,Msila, Algeria

Biometric authentication has demonstrated effectiveness in accurately verifying an individual’s identity. Specifically, palmprint-based biometric systems have gained growing interest in recent years because of their high security, feasibility, and user acceptance. Conventional palmprint recognition approaches involve extracting palmprint features prior to classification, which can influence the recognition performance. In this study, we propose a hybrid model combining deep learning and traditional machine learning techniques for palmprint identification. Features are first extracted using a pre-trained ResNet50 model, then reduced via Principal Component Analysis (PCA), and finally classified using a Support Vector Machine (SVM) with a linear kernel. The experiments are conducted on publicly available contactless datasets, namely Tongji and BMPD, under different data split ratios. Performance is evaluated using accuracy, precision, recall, F1-score, and ROC-AUC analysis. The experimental results show that the proposed approach achieves an accuracy exceeding 99.3% across all tests, with an AUC of 1.00, demonstrating the effectiveness of combining CNN-based feature extraction with classical machine learning techniques for palmprint recognition.

Citation

KHEIRA FAIROUZ Bedjekina , BILAL Attallah , BRIK Youcef , FAYEK Maarfi , ,(2025-11-25), A Hybrid ResNet50-PCA-SVM Model for Accurate Palmprint Recognition,The 2nd International Workshop on Machine Learning and Deep Learning (WMLDL25),University of Mohamed Boudiaf of M'sila

Accurate classification of brain tumors from Magnetic Resonance Imaging (MRI) is essential for patient prognosis, yet single deep learning models often lack the robustness for clinical certainty. This study introduces FALCON (Fusion of Architecturally Layered Classifiers for Oncology), a novel framework designed to overcome these limitations through a systematic, multi-layered fusion of heterogeneous deep learning architectures. The framework was evaluated using a four-class MRI dataset (glioma, meningioma, pituitary, and no tumor) containing 7,023 images. We first established a baseline by training a diverse cohort of 11 base classifiers: three bespoke Convolutional Neural Networks (CNNs) and eight fine-tuned EfficientNet variants (B0-B7). While the top standalone models achieved excellent accuracies (up to 99.69%), our multi-layered approach demonstrated clear performance gains. Homogeneous ensembles of similar models improved the accuracy to 99.85%. Critically, the final layer of the FALCON framework, which fuses architecturally diverse custom CNNs and EfficientNets, achieved a flawless 100% classification accuracy. These results establish that a systematic, multi-layered fusion of heterogeneous models is a superior strategy for maximizing diagnostic certainty and provides a robust methodology for developing clinical-grade AI in neuro-oncology.

Citation

BRIK Youcef , , (2025-09-19), FALCON: A multi-layered fusion ensemble for high-certainty brain tumor diagnosis from MRI, Journal of Radiation Research and Applied Sciences, Vol:18, Issue:4, pages:101940, ELSEVIER

Biometrics has emerged as a pervasive technology for secure and non-invasive identity verification. Palmprint recognition, in particular, offers a rich set of creases and texture features that enhance robustness against spoofing. However, despite advances in deep learning, it remains unclear whether off-the-shelf transfer learning yields superior performance on palmprint datasets. In this work, we conduct a comparative study on the Birjand University Mobile Palmprint Database (BMPD) by applying transfer learning with pre-trained convolutional neural networks (VGG16, ResNeXt50, MobileNetV2, and DenseNet121), optimizing hyperparameters via grid search, and evaluating using accuracy, precision, recall, and F1 score. Our experiments show that DenseNet121 with the Adam optimizer achieved the highest accuracy of 99.38% compared to the other models used. However, with the RMSprop optimizer, MobileNetV2 achieved the best accuracy, also with a value of 99.38%. Our study demonstrates that transfer learning techniques can be a good choice for palmprint recognition.

Citation

KHEIRA FAIROUZ Bedjekina , BILAL Attallah , BRIK Youcef , Cheyma nadir , ,(2025-06-11), Palmprint recognition systems using transfer learning techniques: A comparative study,Third national Conference on Materials Sciences And Engineering, (MSE’25),University of Hassiba Benbouali of Chlef (online presentation)

Breast cancer ranks second in fatality among women. Conventional diagnostic methods are time-consuming, exhausting, and expensive, potentially leading to delayed treatment or misdiagnosis. Machine learning (ML) and deep learning (DL) methods have shown outstanding potential. However, they face challenges like feature identification and selection, data imbalance, and parameter optimization. Unlike most known solutions that have explored these stages for breast cancer prediction, either separately or in limited combinations, our approach simultaneously tackles these critical issues using multi-stage optimization architecture. Three well-established techniques, namely correlation analysis-based feature selection (CFS), LASSO regression, and mutual information (MI), are used for FS. Data balancing is performed using both oversampling and undersampling techniques, including the synthetic minority oversampling technique (SMOTE), k-nearest neighbor oversampling (KNNOR), and random undersampling (RUS). Finally, hyperparameter optimization (HPO) is carried out by adopting various methods including grid search, random search, Bayesian optimization, and semi-automatic to maximize the classification performance of seven renowned ML algorithms (logistic regression, decision tree, random forest, support vector machine, Naïve Bayes, k-nearest neighbor, and eXtreme gradient boosting), and a deep neural network (DNN). Through the experiments carried out on four publicly available datasets, including Wisconsin diagnostic breast cancer (WDBC), Wisconsin breast cancer dataset (WBCD), Wisconsin prognostic breast cancer (WPBC), and Breast cancer coimbra (BCC), the obtained results clearly demonstrate the superiority of the proposed method over the state-of-the-art methods.

Citation

BRIK Youcef , , (2025-05-12), A Multi-stage Optimization Architecture for Effective Breast Cancer Diagnosis Based on Deep Neural Networks, Arabian Journal for Science and Engineering, Vol:50, Issue:1, pages:17943–17968, SPRINGER

This paper investigates the application of deep learning for multi-class classification of lung and colon cancer histopathological images using the LC25000 dataset. The dataset contains 25,000 images equally distributed across five classes: benign and adeno-carcinoma for both lung and colon tissues, and squamous cell carcinoma for lung tissue. Preprocessing involved converting labels to one-hot encoding and removing 1280 identified duplicate images to prevent data leakage. Three models were trained and evaluated: a baseline CNN, an enhanced CNN with architectural modifications and regularization, and a transfer learning model utilizing ResNet50. The baseline model, using Adam optimizer and categorical cross-entropy loss, achieved a test accuracy of 62.6% and validation accuracy of 63.7%. The enhanced model, incorporating increased depth, adjusted kernel size, L2 regularization, dropout, and the Adamax optimizer with early stopping, reached a test accuracy of 97.9% and validation accuracy of 98.1%. Finally, the transfer learning model with ResNet50, fine-tuned with additional dense layers, dropout, and early stopping, achieved near-perfect performance with 98.9% test accuracy and 99.3% validation accuracy. This study demonstrates the significant performance improvement achieved through architectural enhancements and transfer learning, highlighting the potential of deep learning for automated diagnosis of cancer from histopathological images. The removal of duplicate images proved essential for accurate performance evaluation and preventing artificially inflated results due to data leakage.

Citation

BRIK Youcef , ,(2025-04-25), Lung and Colon Cancer Histopathological Image Classification Using Deep Learning Approaches,The 5th International Conference on Artificial Intelligence, Smart Technologies and Engineering Applications (INVENT25),Italy

Alzheimer’s disease (AD) is a chronic, progressive neurodegenerative disorder that typically affects elderly individuals. Detecting Alzheimer’s using plasma proteins is a critical step toward improving treatment results for this disease. This study aims to use computational algorithms to explore the relationship between plasma proteins and AD progression by identifying a panel of plasma proteins that can serve as biomarkers for tracking and diagnosing AD. We applied two feature selection methods, Sequential Backward Feature Selection (SBFS) and Analysis of Variance (ANOVA) to extract significant proteins from a dataset of 146 proteins. The data was collected from the plasma of 566 individuals, comprising both Alzheimer’s patients and healthy controls. The SBFS technique generated all possible combinations of protein groups from the 146 proteins, which were then trained and tested using five machine learning models: Decision Tree, Random Forest, Extremely Randomized Trees, Extreme Gradient Boosting, and Adaptive Boosting. Subsequently, ANOVA was applied to refine and reduce the selected panel size. Finally, we used XGBoost and AdaBoost models to validate the final panel. The findings introduce a plasma protein panel consisting of A2Macro, BNP, BTC, PPP, and PYY proteins for diagnosing AD. This panel achieved a sensitivity of 88.88%, a specificity of 66.66%, and an AUC of 0.85. These results demonstrate that plasma protein biomarkers can facilitate timely interventions, potentially slowing disease progression and improving patient outcomes. This non-invasive and affordable diagnostic method has the potential to make Alzheimer’s screening accessible to a broader population.

Citation

BRIK Youcef , , (2025-02-05), Identification of Plasma Proteins Associated with Alzheimer's Disease Using Feature Selection Techniques and Machine Learning Algorithms, Science, Engineering and Technology, Vol:5, Issue:1, pages:192–202, SET JOURNAL

This paper investigates the application of deep learning for multi-class classification of lung and colon cancer histopathological images using the LC25000 dataset. The dataset contains 25,000 images equally distributed across five classes: benign and adenocarcinoma for both lung and colon tissues, and squamous cell carcinoma for lung tissue. Preprocessing involved converting labels to one-hot encoding and removing 1280 identified duplicate images to prevent data leakage. Three models were trained and evaluated: a baseline CNN, an enhanced CNN with architectural modifications and regularization, and a transfer learning model utilizing ResNet50. The baseline model, using Adam optimizer and categorical cross-entropy loss, achieved a test accuracy of 62.6% and validation accuracy of 63.7%. The enhanced model, incorporating increased depth, adjusted kernel size, L2 regularization, dropout, and the Adamax optimizer with early stopping, reached a test accuracy of 97.9% and validation accuracy of 98.1%. Finally, the transfer learning model with ResNet50, fine-tuned with additional dense layers, dropout, and early stopping, achieved near-perfect performance with 98.9% test accuracy and 99.3% validation accuracy. This study demonstrates the significant performance improvement achieved through architectural enhancements and transfer learning, highlighting the potential of deep learning for automated diagnosis of cancer from histopathological images. The removal of duplicate images proved essential for accurate performance evaluation and preventing artificially inflated results due to data leakage.

Citation

BRIK Youcef , ,(2024-12-09), Lung and Colon Cancer Histopathological Image Classification Using Deep Learning Approaches,National Conference of Advanced Systems in Electrical Engineering (NCASEE'24),Boumerdes

Early and accurate brain tumor detection is critical for effective treatment planning and improving patient outcomes. Traditional diagnostic methods, such as biopsies, are invasive and can delay timely intervention. This research proposes an automated, non-invasive approach for brain tumor classification using Magnetic Resonance Imaging (MRI) and a deep learning model based on transfer learning with Efficient- NetB0. We curated a comprehensive dataset of 3264 MRI scans, encompassing four distinct categories: glioma, meningioma, pituitary tumors, and healthy brain tissues. Images were acquired in various planes (sagittal, axial, and coronal) and pre-processed to ensure consistency and enhance model performance. Data augmentation techniques, including rotation and resizing, were employed to increase the dataset’s diversity and improve the model’s robustness. The EfficientNetB0 architecture, renowned for its computational efficiency and scalability achieved through compound scaling, serves as the foundation of our model. We leveraged transfer learning by utilizing pre-trained weights from ImageNet and fine-tuning the model with a custom classification head comprising a Global Average Pooling layer, a Dropout layer for regularization, and a Dense layer with SoftMax activation for multi-class classification. Performance evaluation on a held-out test set demonstrated a remarkable accuracy of approximately 97% in classifying tumor types. Furthermore, we analyzed the model’s performance using precision, recall, F1-score, and a confusion matrix to provide a comprehensive assessment of its diagnostic capabilities. This research highlights the potential of transfer learning with EfficientNetB0 for developing accurate and efficient automated brain tumor classification systems. Our findings suggest that this approach can contribute significantly to improved diagnostic support, potentially reducing the need for invasive procedures and facilitating timely treatment interventions.

Citation

BRIK Youcef , ,(2024-12-03), Automated Brain Tumor Classification using a Fine-Tuned EfficientNet Model,The first National Conference on Artificial Intelligence and its Applicat1ons,Constantine 1

This paper proposes a novel two-stage approach to improve brain tumor classification accuracy using the Br35H MRI Scan Dataset. The first stage employs advanced image enhancement algorithms, GFPGAN and Real-ESRGAN, to enhance the image dataset’s quality, sharpness, and resolution. Nine deep learning models are then trained and tested on the enhanced dataset, experimenting with five optimizers. In the second stage, ensemble learning algorithms like weighted sum, fuzzy rank, and majority vote are used to combine the scores from the trained models, enhancing prediction results. The top 2, 3, 4, and 5 classifiers are selected for ensemble learning at each rating level. The system’s performance is evaluated using accuracy, recall, precision, and F1-score. It achieves 100% accuracy when using the GFPGAN-enhanced dataset and combining the top 5 classifiers through ensemble learning, outperforming current methodologies in brain tumor classification. These compelling results underscore the potential of our approach in providing highly accurate and effective brain tumor classification.

Citation

BRIK Youcef , BILAL Attallah , , (2024-12-01), MRI-based brain tumor ensemble classification using two stage score level fusion and CNN models, Egyptian Informatics Journal, Vol:28, Issue:1, pages:100565, ELSEVIER

Iris recognition, a biometric modality, has grown significantly in recent years. Traditional techniques relied on feature extraction methods and classical machine learning classifiers. However, deep learning models, particularly Convolutional Neural Networks (CNNs), have exhibited remarkable performance in learning discriminative features from iris images, making them robust to variations in imaging conditions and achieving state-of-the-art recognition accuracy. Nonetheless, injuries or occlusions affecting the iris can lead to increased error rates due to missing information. This study presents and evaluates CNN-based models for iris recognition. Various CNN architectures were employed for feature extraction, and the best-performing model was selected. Features from the left and right iris (one instance) were fused to create a multiple-instance system, enhancing recognition performance. The proposed approach was tested using the SDUMLA-HMT iris dataset. The results demonstrate that our system achieves an accuracy of 100%. Comparative analysis with existing methods indicates that our system outperforms the current state-of-the-art techniques for iris recognition.

Citation

BRIK Youcef , ,(2024-11-18), A Robust Convolutional Neural Network for Iris Recognition system,National Conference of Applied Sciences and Engineering NCASE ’24,AGIERS

Diabetic retinopathy (DR) presents a significant threat to global visual health. This study introduces a method leveraging deep learning and machine learning to enhance DR detection accuracy. By combining pre-trained CNNs (MobileNetV2, ResNet50, and Xception) with high performance classifiers like SVM and KNN, our method significantly improves diagnostic performance. The combination of ResNet50 and SVM achieved a 95.90% accuracy in detecting retinal abnormalities for the APTOS 2019 Blindness Detection dataset, demonstrating its superiority over current diagnostic techniques.

Citation

BRIK Youcef , ,(2024-11-17), An innovative CNN-SVM hybrid model for enhanced diabetic retinopathy detection,Inductics conference,M'SILA

An accurate personal identification system helps control access to secure information and data. Biometric technology mainly focuses on the physiological or behavioural characteristics of the human body. This paper investigates the Finger Knuckle Print (FKP) biometric device based on the feature extraction technique. This FKP authentication method includes all the essential processes, such as preprocessing, feature extraction and classification. The features of the FKP application are investigated. Finally, this paper proposes the selection of the best feature extraction based on FKP recognition efficiency. The primary purpose of this paper is to use the Local Binary Patterns (LBP), Binarized Statistical Image Features (BSIF), and Gabor filters and define which helps to increase the False Acceptability Rate (FAR) and Genuine Acceptability Rate (GAR). This latest FKP selection shows better results as this concept shows promising results in recognizing a person's fingerknuckle print.

Citation

BRIK Youcef , BILAL Attallah , , (2024-09-01), Gabor, LBP, and BSIF features: Which is more appropriate for finger-knuckles-print recognition?, Przegląd Elektrotechniczny, Vol:2024, Issue:9, pages:62, Wydawnictwo SIGMA

Biometric systems play a crucial role in securely recognizing an individual’s identity based on physical and behavioral traits. Among these methods, finger vein recognition stands out due to its unique position beneath the skin, providing heightened security and individual distinctiveness that cannot be easily manipulated. In our study, we propose a robust biometric recognition system that combines a lightweight architecture with depth-wise separable convolutions and residual blocks, along with a machine-learning algorithm. This system employs two distinct learning strategies: single-instance and multi-instance. Using these strategies demonstrates the benefits of combining largely independent information. Initially, we address the problem of shading of finger vein images by applying the histogram equalization technique to enhance their quality. After that, we extract the features using a MobileNetV2 model that has been fine-tuned for this task. Finally, our system utilizes a support vector machines (SVM) to classify the finger vein features into their classes. Our experiments are conducted on two widely recognized datasets: SDUMLA and FV-USM and the results are promising and show excellent rank-one identification rates with 99.57% and 99.90%, respectively.

Citation

BRIK Youcef , BILAL Attallah , , (2024-08-14), A sequential combination of convolution neural network and machine learning for finger vein recognition system, Signal, Image and Video Processing, Vol:18, Issue:11, pages:8267-8278, SPRINGER

The fundus images of patients with Diabetic Retinopathy (DR) often display numerous lesions scattered across the retina. Current methods typically utilize the entire image for network learning, which has limitations since DR abnormalities are usually localized. Training Convolutional Neural Networks (CNNs) on global images can be challenging due to excessive noise. Therefore, it's crucial to enhance the visibility of important regions and focus the recognition system on them to improve accuracy. This study investigates the task of classifying the severity of diabetic retinopathy in eye fundus images by employing appropriate preprocessing techniques to enhance image quality. We propose a novel two-branch attention-guided convolutional neural network (AG-CNN) with initial image preprocessing to address these issues. The AG-CNN initially establishes overall attention to the entire image with the global branch and then incorporates a local branch to compensate for any lost discriminative cues. We conduct extensive experiments using the APTOS 2019 DR dataset. Our baseline model, DenseNet-121, achieves average accuracy/AUC values of 0.9746/0.995, respectively. Upon integrating the local branch, the AG-CNN improves the average accuracy/AUC to 0.9848/0.998, representing a significant advancement in state-of-the-art performance within the field.

Citation

BRIK Youcef , BILAL Attallah , , (2024-06-27), Two-stage deep learning classification for diabetic retinopathy using gradient weighted class activation mapping, Automatika: Journal for Control, Measurement, Electronics, Computing and Communications, Vol:65, Issue:3, pages:1284-1299, Taylor & Francis

Diabetic retinopathy (DR) is a significant eye-related disorder caused by accumulated damage to small retinal blood vessels, often leading to severe visual impairment. Early detection and accurate diagnosis of DR are crucial for preventing disease progression and ensuring timely treatment. Automated diagnostic systems, especially those based on deep learning techniques, have shown great promise in aiding ophthalmologists in this regard. This work proposes an automated DR detection system using a pre-trained Inception-v3 deep neural network model. The model is fine-tuned to classify retinal fundus images from the Aptos 2019 dataset into two categories: "With DR" and "No DR." By leveraging the powerful feature extraction capabilities of the Inception-v3 model, our system effectively identifies small lesions in retinal images that are indicative of DR. The model's performance is evaluated based on Precision, Recall, F1-score, and AUC. The Confusion Matrix and ROC curves are also used to visualize and further validate the model's performance. Our proposed system achieves an accuracy of 98.36%, demonstrating its efficacy in DR detection. The system's capability is further validated by comparing it with other established convolutional neural network models, highlighting its superior performance. These results underscore the potential of our Inception-v3-based approach to provide efficient and accurate DR diagnosis, supporting ophthalmologists in the early detection and management of diabetic retinopathy.

Citation

BRIK Youcef , ,(2024-06-16), Detection of Diabetic Retinopathy Using a Pre-trained Inception-v3 Model,3rd International Conference on Frontiers in Academic Research,KONYA

Diabetic retinopathy (DR) is recognized as a significant threat to global visual health among diabetes patients worldwide, necessitating an urgent need for efficient detection methods. Manual methods of diagnosis have limitations, resulting in delayed diagnosis, especially in areas with limited access to healthcare services. Deep learning techniques such as convolutional neural networks (CNNs), provide promising performance in various medical applications. This study proposes an innovative framework that leverages deep learning and machine learning techniques to enhance the diagnostic accuracy and detection efficiency of DR. The framework combines pre-trained CNNs with robust machine learning classifiers to enhance prediction capabilities, incorporating Support Vector Machines (SVMs) and K-Nearest Neighbor (KNN), stacking their predictions for improved performance. Additionally, three CNN models—MobileNetV2, ResNet50, and Xception—are employed to extract pertinent features from retinal images within the DR 224x224 Gaussian Filtered dataset. The combination of ResNet50 and SVM achieved the highest accuracy rate of 95.22% in detecting retinal abnormalities within DR images. Comprehensive validation of our framework highlights its superiority over current diagnostic techniques. By Integrating advanced deep learning architectures with machine learning classifiers, this study marks a significant advancement in the automated detection of Diabetic Retinopathy, potentially enhancing healthcare outcomes for diabetic patients globally.

Citation

BRIK Youcef , ,(2024-06-16), Enhancing Diabetic Retinopathy Detection Using a Hybrid Framework Integrating Machine Learning Classifiers and Advanced Feature Extraction Techniques,3rd International Conference on Frontiers in Academic Research,KONYA

These days, there is increasing in crime, piracy, and security issues across different sectors, highlighting the necessity for reliable identity recognition methods. Traditional security systems, which rely on pre-existing information or token-based access, often struggle to distinguish between authorized individuals and fraudsters. In this research, we investigate deep-learning palmprint recognition systems as a promising alternative. Palmprint modality offers several advantages, including uniqueness, security, ease of use, and non-intrusiveness. Our approach explores fine-tuning for feature extraction and two strategies: learning from single and multiple instances. The proposed system is divided into two parts. First, we employ three convolutional neural network models (VGG16, VGG19, and MobileNetV2) for feature extraction and classification. Based on initial experiment results, we select the best-performing models and apply a feature fusion technique to concatenate the features extracted from the left and right palmprint instances, thus creating our multiple-instance system. Experimental results using the PolyU palmprint database demonstrate the effectiveness of our method. With single instance learning, we achieved accuracies of 95% and 96% (VGG16), 93% and 94% (VGG19), and 97.50% and 96% (MobileNetV2). Using multiple instances, we achieved an accuracy of 99,50%. Our proposed work exhibits good performance compared to existing methods, particularly in multi-instance scenarios. This highlights the potential of deep-learning palmprint recognition systems to address identification challenges across various sectors, especially in cases of physical anomalies such as injuries, disfigurements, or scars.

Citation

BRIK Youcef , ,(2024-06-15), Deep neural networks for an accurate palmprint recognition system across multiple instances,3rd International Conference on Frontiers in Academic Research,KONYA

Diabetes or diabetes mellitus (DM) is a worldwide issue of pandemic scale. The effect or illness of this disease on the human eye is called diabetic retinopathy (DR) which can be diagnosed from the images of the eye fundus. The fundus images of patients with Diabetic Retinopathy (DR) often display numerous lesions scattered across the retina. This study investigates the task of classifying the presence of diabetic retinopathy in eye fundus images by first, employing appropriate preprocessing techniques to enhance image quality such as CLAHE and LoG techniques. Then, we propose a novel fusion system that uses both: features extracted using Convolutional neural networks (densenet-121), and hand-crafted texture features (such as LBP, LPQ and BSIF). These two types of features are then used as the input of a machine learning system (using SVM, KNN, MLP) that has the task of classifying the presence of DR in the fundus images (Binary classification). After much extensive tests, we found that our system has great performance both in calculation time and accuracy, the best accuracy results was found using SVM as the ML technique (using Poly kernel and the parameters C=100) and LBP as the hand-crafted feature extracted with the accuracy of 94.32%, representing a significant advancement in state-of-the-art performance within the field.

Citation

BRIK Youcef , ,(2024-06-15), Combining CNN with hand-crafted features for Diabetic retinopathy classification,3rd International Conference on Frontiers in Academic Research,KONYA

Ear recognition is a complex research domain within biometrics, aiming to identify individuals using their ears in uncontrolled conditions. Despite the exceptional performance of convolutional neural networks (CNNs) in various applications, the efficacy of deep ear recognition systems is nascent. This paper proposes a two-step ear recognition approach. The initial step employs deep convolutional generative adversarial networks (DCGANs) to enhance ear images. This involves the colorization of grayscale images and the enhancement of dark shades, addressing visual imperfections. Subsequently, a feature extraction and classification technique, referred to as Mean-CAM-CNN, is introduced. This technique leverages mean-class activation maps in conjunction with CNNs. The Mean-CAM approach directs the CNN to focus specifically on relevant information, extracting and assessing only significant regions within the entire image. The process involves the implementation of a mask to selectively crop the pertinent area of the image. The cropped region is then utilized to train a CNN for discriminative classification. Extensive evaluations were conducted using two ear recognition datasets: mathematical analysis of images (MAI) and annotated web ears (AWEs). The experimental results indicate that the proposed approach shows notable improvements and competitive performance: the Rank-1 recognition rates are 100.00% and 76.25% for MAI and AWE datasets, respectively.

Citation

BRIK Youcef , , (2024-05-14), Boosting the Performance of Deep Ear Recognition Systems Using Generative Adversarial Networks and Mean Class Activation Maps, Applied Sciences, Vol:14, Issue:10, pages:4162, MDPI

Accurate classification of brain tumors from magnetic resonance imaging (MRI) scans is critical for selecting appropriate treatment strategies. In this work, three convolutional neural network (CNN) architectures were developed and trained on a multi-class brain tumor MRI dataset containing four types of images: no tumor, meningioma, glioma, and pituitary tumors. The CNN models achieved test accuracy scores above 99%. Ensemble techniques using average and majority voting were applied to boost performance further and integrate the three models' predictions. The ensemble approach provided a slight but noticeable improvement, with the best model reaching 99.46% accuracy. Overall, the deep CNN models demonstrated excellent capabilities for distinguishing between the multiple tumor classes from MRI scans. The ensemble method offered a way to extract incremental benefits by combining multiple trained models.

Citation

BRIK Youcef , ,(2023-12-05), Ensemble Learning vs. Convolutional Neural Networks for Multiclass Brain Tumor Classification of MRI Images,International Conference on Science, Technology, Engineering and Management,Istanbul

Artificial intelligence (AI) and biological data analysis have advanced significantly in recent years, providing new opportunities for disease diagnosis, treatment planning, and patient monitoring. Biomedical data, which includes a variety of medical imaging and clinical data, is crucial in revealing the complex mechanisms underlying different health disorders. The use of AI methods, especially deep learning and transfer learning, has emerged as a game-changing method for gleaning relevant information from this vast amount of biomedical data. The present work focuses on the application of deep learning to diagnose and analyze many different diseases for helping healthcare professionals to take care of people. Our proposed system explores the application of deep transfer learning models, such as ResNet50, DenseNet-121, and EfficientNet-B3, in analyzing brain tumors, Alzheimer's disease, respiratory diseases, skin cancer, and gastrointestinal diseases. This analysis can give the healthcare professionals a precise information concerning the presence/absence of disease, the severity of tumor, the type of illness and the treatment planning. Furthermore, many preprocessing operations such as data augmentation and image filtering have been involved in order to enhance the decision accuracy. The experiments evaluated and conducted on real databases showed that our system can effectively help medical staff in their daily work for taking care of people.

Citation

BRIK Youcef , ,(2023-12-03), Helping System for Brain Disease Diagnosis Using Deep Learning,International Conference on Science, Technology, Engineering and Management,Istanbul

Medical professionals have become interested in using deep learning techniques in the treatment of many diseases, such as diabetic retinopathy (DR), because they come with great data analysis capabilities. We propose in this paper a detection system for DR (the presence of this disease or not) using deep learning models. We used the APTOS2019 dataset from Kaggle, which includes high-resolution retinal pictures, to evaluate this approach. Then, we use transfer learning to characterize the DR images with discriminating features. Our experiments have been conducted on five models: VGG16, VGG19, InceptionV3, Xception, and MobileNetV2. The obtained results are very satisfactory in terms of accuracy.

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BRIK Youcef , ,(2023-10-23), Deep Learning for Biomedical and Biometrics Applications,CAIRO Talk series,Kuala Lampur

Internet public social media and forums provide a convenient channel for people concerned about public health issues, such as COVID-19, to share and discuss information/misinformation with each other. In this paper, we propose a natural language processing (NLP) method based on Bidirectional Long Short-Term Memory (Bi-LSTM) technique to perform sentiment classification and uncover various issues related to COVID-19 public opinions. Bi-LSTM is an improved version of conventional LSTMs for generating the output from both left and right contexts at each time step. We experimented with real datasets extracted from Twitter and Reddit social media platforms, and our experimental results showed improved metrics compared with the conventional LSTM model as well as recent studies available in the literature. The proposed model can be used by official institutions to mitigate the effects of negative messages and to understand peoples’ concerns during the pandemic. Furthermore, our findings shed light on the importance of using NLP techniques to analyze public opinion and to combat the spreading of misinformation and to guide health decision-making.

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BRIK Youcef , Arbane Mohamed, Rachid Benlamri, Ayman Diyab Alahmar, , (2023-02-01), Social media-based COVID-19 sentiment classification model using Bi-LSTM, Expert Systems with Applications, Vol:212, Issue:2023, pages:118710, Elsevier

Monitoring of water quality is one of the world’s main intentions for countries. Classification techniques based on support vector machines (SVMs) and artificial neural network (ANN) has been widely used in several applications of water research. Water quality assessment with high accuracy and efficiency with innovational approaches permitted us to acquire additional knowledge and information to obtain an intelligent monitoring system. In this paper, we present the use of principal component analysis (PCA) combined with SVM and ANN with decision templates combination data fusion method. PCA was used for features selection from original database. The multi-layer perceptron network (MLP) and the one-against-all strategy for SVM method have been widely used. Decision templates are applied to increase the accuracy of the water quality classification. The specific classification approach was employed to assess the water quality of the Tilesdit dam in Algeria as a study area, defined with a dataset of eight physicochemical parameters collected in the period 2009–2018, such as temperature, pH, electrical conductivity, and turbidity. The selection of the excellent parameters of the used models can be improving the performance of classification process. In order to assess their results, an experiment step using collected dataset corresponding to the accuracy and running time of training and test phases, and robustness to noise, is carried out. Various scenarios are examined in comparative study to obtain the most results of decision step with and without feature selection of the input data. From the results, we found that the integration of SVM and ANN with PCA yields accuracy up than 98%. The combination by decision templates of two classifiers SVM and ANN with PCA yields an accuracy of 99.24% using k-fold cross-validation. The combination data fusion enhanced expressively the results of the proposed monitoring framework that had proven a considerable ability in surface water quality assessment.

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BRIK Youcef , , (2023-01-01), A decision fusion method based on classification models for water quality monitoring, Environmental Science and Pollution Research, Vol:30, Issue:, pages:22532–22549, Springer

Damage to the retina from diabetes can lead to permanent vision loss due to a condition known as diabetic retinopathy. In order to avoid this, it is essential to diagnose this disease early. To address these problems, this paper proposes a two-branch Grad-CAM attention-guided convolution neural network (AG-CNN) with initial CLAHE image preprocessing. The AG-CNN first builds a general attention to the entire image with the global branch, in order to further concentrate the system's attention on the localized areas of the problems, the system isolate the important regions (ROIs) of the global image and then feeds them to a local branch. This extensive experiment is based on the APTOS 2019 DR dataset. In order to start, we offer a solid global baseline that, using DenseNet-121 as a starting point, produced average accuracy/AUC values of 0.9746/0.995, respectively. The average accuracy and AUC of the AG-CNN are increased to 0.9848 and 0.998, respectively, after creating the local branch. which represents a new state-of-the-art in the field.

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Mohamed Abderaouf Moustari , BRIK Youcef , BILAL Attallah , ,(2022-11-26), Enhancement of diabetic retinopathy classification using attention guided convolution neural network,ICATEEE2022,M'sila, Algeria

Deep learning based framwork for automatic diabetic retinopathy detection

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BRIK Youcef , BILAL Attallah , Ishaq AICHE , ,(2022), Deep learning based framwork for automatic diabetic retinopathy detection,ICCTA 2022,Alexendrie_Egypt

Finger vein based cnn for human recognition

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Cheyma nadir , BILAL Attallah , BRIK Youcef , ,(2022), Finger vein based cnn for human recognition,ICATEEE2022,M'sila-Algeria

Transfer learning for diabetic retinopathy detection

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Ishaq AICHE , BRIK Youcef , BILAL Attallah , ,(2022), Transfer learning for diabetic retinopathy detection,ICATEEE2022,M'sila-Algeria

Brain tumor classification based deep transfer learning

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BILAL Attallah , BRIK Youcef , oussama.bougeurra@univ-msail.dz, ,(2022), Brain tumor classification based deep transfer learning,ICATEEE2022,M'sila_Algeria

Transfer learning approche for alzhimer's dicease diagnosis using mri image

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BRIK Youcef , BILAL Attallah , DJERIOUI Mohamed , rafik.zouaoui@univ-msila.dz, ,(2022), Transfer learning approche for alzhimer's dicease diagnosis using mri image,ICATEEE2022,M'sila_Algeria

Ear Recognition using Ensemble of Deep Features and Machine Learning Classifiers

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Cheyma nadir , BILAL Attallah , BRIK Youcef , ,(2022), Ear Recognition using Ensemble of Deep Features and Machine Learning Classifiers,ICCTA 2022,Alexandria, Egypt

This paper deals with

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BRIK Youcef , ,(2022), Performance Analysis ofTwin-SupportVector Machine in Breast Cancer Prediction,ICATEEE2022,Algeria

One of the main reasons for disability and premature mortality in the world is diabetes disease, which can cause different sorts of damage to organs such as kidneys, eyes, and heart arteries. The deaths by diabetes are increasing each year, so the need to develop a system that can effectively diagnose diabetes patients becomes inevitable. In this work, an efficient medical decision system for diabetes prediction based on Deep Neural Network (DNN) is presented. Such algorithms are state-of-the-art in computer vision, language processing, and image analysis, and when applied in healthcare for prediction and diagnosis purposes, these algorithms can produce highly accurate results. Moreover, they can be combined with medical knowledge to improve decision-making effectiveness, adaptability, and transparency. A performance comparison between the DNN algorithm and some well-known machine learning techniques as well as the state-of-the-art methods is presented. The obtained results showed that our proposed method based on the DNN technique provides promising performances with an accuracy of 99.75% and an F1-score of 99.66%. This improvement can reduce time, efforts, and labor in healthcare services as well as increasing the final decision accuracy.

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Tawfiq Beghriche , DJERIOUI Mohamed , BRIK Youcef , BILAL Attallah , , (2021-12-17), An efficient prediction system for diabetes disease based on deep neural network, Complexity, Vol:2021, Issue:1, pages:1-14, Hindawi

Heart disease is the leading cause of death in the world according to the World Health Organization (WHO). Researchers are more interested in using machine learning techniques to help medical staff diagnose or detect heart disease early. In this paper, we propose an efficient medical decision support system based on twin support vector machines (Twin-SVM) for heart disease diagnosing with binary target (i.e. presence or absence of disease). Unlike conventional support vector machines (SVM) that finds only one optimal hyper- plane for separating the data points of first class from those of second class, which causes inaccurate decision, Twin-SVM finds two non-parallel hyper-planes so that each one is closer to the first class and is as far from the second class as possible. Our experiments are conducted on real heart disease dataset and many evaluation metrics have been considered to evaluate the performance of the proposed method. Furthermore, a comparison between the proposed method and several well-known classifiers as well as the state-of-the-art methods has been performed. The obtained results proved that our proposed method based on Twin-SVM technique gives promising performances better than the state-of-the-art. This improvement can seriously reduce time, materials, and labor in healthcare services while increasing the final decision accuracy.

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BRIK Youcef , DJERIOUI Mohamed , BILAL Attallah , , (2021), Efficient heart disease diagnosis based on twin support vector machine, DIAGNOSTYKA, Vol:22, Issue:3, pages:3-11, PTDT

One of the most leading death causes in the world is brain tumor. Solving brain tumor segmentation and classification by relying mainly on classical medical image processing is a complex and challenging task. In fact, medical evidence shows that manual classification with human-assisted support can lead to improper prediction and diagnosis. This is mainly due to the variety and the similarity of tumors and normal tissues. Recently, deep learning techniques showed promising results towards improving accuracy of detection and classification of brain tumor from magnetic resonance imaging (MRI). In this paper, we propose a deep learning model for the classification of brain tumors from MRI images using convolutional neural network (CNN) based on transfer learning. The implemented system explores a number of CNN architectures, namely ResNet, Xception and MobilNet-V2. This latter achieved the best results …

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BRIK Youcef , DJERIOUI Mohamed , ,(2021), Transfer learning for automatic brain tumor classification using MRI images,2020 2nd International Workshop on Human-Centric Smart Environments for Health and Well-being (IHSH),Boumerdes, Algeria

Nowadays, one of the main reasons for disability and mortality premature in the world is the heart disease, which make its prediction is a critical challenge in the area of healthcare systems. In this paper, we propose a heart disease prediction system based on Neighborhood Component Analysis (NCA) and Support Vector Machine (SVM). In fact, NCA is used for selecting the most relevant parameters to make a good decision. This can seriously reduce the time, materials, and labor to get the final decision while increasing the prediction performance. Besides, the binary SVM is used for predicting the selected parameters in order to identify the presence/absence of heart disease. The conducted experiments on real heart disease dataset show that the proposed system achieved 85.43% of prediction accuracy. This performance is 1.99% higher than the accuracy obtained with the whole parameters. Also, the proposed system outperforms the state-of-the-art heart disease prediction.

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DJERIOUI Mohamed , BRIK Youcef , Mohamed LADJAL , BILAL Attallah , , (2019), Neighborhood Component Analysis and Support Vector Machines for Heart Disease Prediction, Ingénierie des Systèmes d’Information (ISI), Vol:24, Issue:6, pages:591-595, International Information and Engineering Technology Association (IIETA)

Multimodal biometrics is an evolving technology in the fields of security. Biometrics system reduces the effort of remember a memorable password. Multimodal biometrics system uses two or more traits for efficient recognition. This paper presents a hand biometric system by fusing information of palmprint and finger knuckle. To this end, BSIF ( Binarized Statistical Image Features) filter and LBP (Local binary patterns) coefficients are employed to obtain the Finger-knuckle-print and palm-print traits, and subsequently selection of the features vector is conducted with PCA (Principal Component Analysis) transforms in higher coefficients. To match the finger knuckle or palm-print feature vector, the (ELM) Extreme learning machine is applied. According to the experiment outcomes, the proposed system not only has a significantly high recognition rate but it also affords greater security compared to the single biometric system.

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BILAL Attallah , BRIK Youcef , DJERIOUI Mohamed , ABDELWAHHAB BOUDJELAL , ,(2019), Fusing Palmprint, Finger-knuckle-print for Bi-modal Recognition System Based on LBP and BSIF,International Conference on Image and Signal Processing and their Applications,Mostaganem, Algeria, Algeria

A Biometrics identification system is refers to the automatic recognition of individual person based on their characteristics. Basically biometrics system has two broad areas namely unimodal biometric system and multimodal biometric system. However, a reliable recognition system requires multiple resources [1]. Although multimodality improves the accuracy of the systems, it occupies a large memory space and consumes more execution time considering the collected information from different resources. Therefore we have considered the feature selection[2], that is, the selection of the best attributes that enhances the accuracy and reduce the memory space as a solution. As a result, acceptable recognition performances with less forge and steal can be guaranteed. In this work we propose an identification system using multimodal fusion of finger-knuckle-print, fingerprint and palmprint by adopting several techniques in feature level for multimodal fusion[3]. A feature level fusion and selection is proposed for the fusion of these three biological traits. The proposed system has been tested on the largest publicly available PolyU [4] and Delhi FKP[5] databases. It has shown good performance.

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BILAL Attallah , HAMZA Bennacer , Mohamed LADJAL , BRIK Youcef , Youssef Chahir, ,(2019), Finger-knuckle-print, Plamprint and Fingerprint for Multimodal Recognition System Based on mRMR features selection,IC2MAS19,Istanbul-Turkey

In this paper, we propose a heart disease prediction system based on Neighborhood Component Analysis (NCA) and Support Vector Machine (SVM). In fact, NCA is used for selecting the most relevant parameters to make a good decision. This can seriously reduce the time, materials, and labor to get the final decision while increasing the prediction performance. Besides, the binary SVM is used for predicting the selected parameters in order to identify the presence/absence of heart disease. The conducted experiments on real heart disease dataset show that the proposed system achieved 85.43% of prediction accuracy. This performance is 1.99% higher than the accuracy obtained with the whole parameters. Also, the proposed system outperforms the state-of-the-art heart disease prediction.

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DJERIOUI Mohamed , BRIK Youcef , Mohamed LADJAL , BILAL Attallah , Youssef Chahir, ,(2019), Heart disease prediction using neighborhood component analysis and support vector machines,The VIIIth International Workshop on Representation, analysis and recognition of shape and motion FroM Imaging data (RFMI 2019),Tunisia

Most of existing approaches in keyword spotting are system-oriented, which did not take into consideration the user’s needs. However, a user may want to find words, sentences, or texts that match his target image in his mind. The challenge here is how to formulate one’s mental image to reach what he is looking for. The key idea is to design and build a model that properly adapts the human reasoning in information searching through an interactive process. We propose a mental model for handwritten keyword spotting based on relevance feedback, feature weighting, and optimization. This model meets simultaneously the user’s needs, the system behavior, and the user–system relationship. In an appropriate feature space, the query is progressively built from user-supplied keywords, old queries, and spotted images. This dynamic process not only converges toward the desired word images, but also helps the hesitant user to clarify progressively what he is looking for. The proposed model was showcased via a user-friendly interface, which we tested including real users on three well-known handwritten datasets; Institute for Communications, Braunschweig University, Germany/École Nationale d’Ingénieurs de Tunis, Tunisia, Institut für informatik und Angewandte Mathematik, and George Washington. The experimental results show that the proposed method provides promising scores with a reasonable number of refinements.

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BRIK Youcef , Djemel Ziou, , (2018), Mental model for handwritten keyword spotting, Journal of Electronic Imaging, Vol:27, Issue:5, pages:053027, SPIE Digital Library (USA)

This paper relates to the study and the realization of a multilevel inverter associated with the card-based control of microcontroller PIC16F876. This is the series connection of three single phase inverter H-bridge with separate continuous voltages. In the theoretical section we presented a study on the model of the inverter and its control strategies, the full wave and the PWM with its variants. In the practical part, after realizing the control cards and power we implemented for control strategies on the PIC16F876.

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FAYSSAL Ouagueni , BRIK Youcef , ,(2012), Réalisation d’un onduleur monophasé cascadé à sept niveaux piloté par le PIC16F876,Deuxième Conférence Internationale sur la Maintenance, la Gestion, la Logistique et l’Electrotechnique,Oran, Algeria