BILAL Attallah

Nadhir Chayma

Deep Learning Approach for Multimodal Biometric Recognition System

Zakaria MOKADEM

DEVELOPMENT AND IMPLEMENTATION OF AN INTELLIGENT SYSTEM FOR PREDICTING ALZHEIMER’S DISEASE

Moustari Mohamed Abderaouf

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

BERRA Ayyoub

Enhancing Brain Tumor Classification Accuracy through Multi-Modal MRI Analysis and Advanced Ensemble Deep Learning

Bouzidi Youssouf

Deep Learning Architectures for Precise Segmentation of Brain Tumors in MRI Images.

Lamri Islam

Empowering Healthcare: A Platform for Innovative Brain Tumor classification

Chergui Asma , Elgharbi Hadjira

Multiple CNN Models For Enhanced Palmprint Recognition

Aymen Abdelkade cherif , Mohamed salim Bensalem

A Feature Extraction Method for Iris Recognition System Based on CNN(Transfer Learning

Bentahar Adel , Djouilem Aboubakar

Deep feature extraction and classification for finger vein images

KHERFI AHMED AYOUB , KOADRI HICHEM

DEEP TRANSFER LEARNING FOR EAR RECOGNATION

Allal bassim , Bouafia fatima ezahra

La Fusion Bimodal pour L’indentification Biométrique

Abdelhafid, Oualid; , Senouci, Abdelkrim

L’identification Biométrique Par Les Veines Des Doigts

Fatma Zohra, MAHDI , Fattoum, TABI

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

BENABDI Mouad

Identification des personnes par Les empreintes d’articulation Des doigts en utilisant le deep Learning

TALEB Adnane

La transformée en ondelettes pour la compression des images colleurs

BENSEDDIK Larbi

L’identification Biométrique Basée Sur Les Empreintes Palmaires

Alzheimer’s disease (AD) is a gradient degeneration of essential cognitive activities such as memory, thinking, and cognition. AD mainly affects elderly individuals and is recognized as the most common cause of dementia. This study investigates the predictive performance of nine supervised machine learning algorithms—Logistic Regression, Decision Tree, Random Forest, K-Nearest Neighbors, Support Vector Machine, Gaussian Naïve Bayes, Multi-Layer Perceptron, eXtreme Gradient Boost, and Gradient Boosting—using neuropsychological assessment data. We applied two classification techniques—binary and multiclass—to classify 1761 subjects into three categories: cognitively normal (CN), mild cognitive impairment (MCI), and Alzheimer's disease (AD). Binary classification tasks focused on CNvsAD and CNvsMCI subsets, while multiclass classification used the full dataset (TriClass). Hyperparameter tuning was performed to optimize model performance. The results indicate that ensemble learning models, particularly Gradient Boosting (GB) and Random Forest (RF), exhibited superior accuracy compared to other algorithms. Most models for the CNvsAD subset achieved the highest accuracy (97.74%), while GB achieved the best performance (94.98%) for the CNvsMCI subset. For multiclass classification, RF achieved the highest accuracy at 84.70%. These findings highlight the robustness and efficiency of ensemble learning algorithms, especially in handling complex, non-linear data structures. This study underscores the potential of RF and GB as reliable tools for early detection and classification of Alzheimer’s disease using neuropsychological data.

Citation

BILAL Attallah , , (2025-12-23), A Comparison of Machine Learning Algorithms for Predicting Alzheimer’s Disease Using Neuropsychological Data, Set jaurnal, Vol:5, Issue:1, pages:177-191, Science, Engineering and Technology

Enhancing Brain Tumor classification accuracy throug multi modal MRI analysis and advanced ensemmble deep learning

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BILAL Attallah , ,(2025-12-10), Enhancing Brain Tumor classification accuracy throug multi modal MRI analysis and advanced ensemmble deep learning,ICATEEE2025,M'sila Algerie

Comparative Evaluation of deep Learning Architecture and ensemble strategie for accurate brain tumor segmentation

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BILAL Attallah , ,(2025-12-10), Comparative Evaluation of deep Learning Architecture and ensemble strategie for accurate brain tumor segmentation,ICATEEE2025,M'sila University

Hight perfarmance diabetic retinopathy detection using mobile net-V3 large with RGB and cluhs fundus image

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BILAL Attallah , ,(2025-12-10), Hight perfarmance diabetic retinopathy detection using mobile net-V3 large with RGB and cluhs fundus image,ICATEEE2025,M'sila University

A multimodal biometric system based on multilevel fine tuning using score fusion

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BILAL Attallah , ,(2025-12-10), A multimodal biometric system based on multilevel fine tuning using score fusion,ICATEEE2025,M'sila University

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

Enhanced diabetic retinopathy classification via mobile net V3 large combined RGB and clahe retinal fundus imagery

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BILAL Attallah , ,(2025-10-29), Enhanced diabetic retinopathy classification via mobile net V3 large combined RGB and clahe retinal fundus imagery,SNMSM,M'sila University

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

BILAL Attallah , , (2025-09-18), 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:11, 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

BILAL Attallah , , (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:50, pages:17943–17968, Springer

Detection diabetic retinopathy in fundus image using the hybrid inception resNet V2 model

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BILAL Attallah , ,(2025-05-06), Detection diabetic retinopathy in fundus image using the hybrid inception resNet V2 model,NC-REAEE'25,M'sila University

Lung and colon cancer Histopathological image classification using deep learning image

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BILAL Attallah , ,(2025-04-25), Lung and colon cancer Histopathological image classification using deep learning image,InVEnT2025,ITALY

Alzheimer’s disease is a gradient degeneration of essential cognitive activities that mainly affects elderly individuals. Diagnosis of Alzheimer’s disease by neuropsychological assessments is considered an important step in disease management. However, using a single neuropsychological assessment technique often leads to a high rate of misdiagnosis of the disease. To tackle this issue, we built a Random Forest machine learning algorithm with five different neuropsychological assessments to predict Alzheimer’s disease. A total of 1761 samples acquired from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database were classified into cognitively normal (CN), mild cognitive impairment (MCI), and Alzheimer’s disease groups. we create three separate subsets, namely CN vs. AD, CN vs. MCI, and AD vs. MCI for train and test the Random Forest model. Our model achieved high accuracy in distinguishing between the healthy and affected groups, it attained an accuracy of 97.74% with the CN vs. AD subset and 94.65% with the CN vs. MCI subset, making it capable of quickly diagnosis of AD. Our study suggests a robust and efficient Random Forest classifier for Alzheimer’s disease prediction.

Citation

BILAL Attallah , ,(2025-04-14), Random Forest Algorithm for Alzheimer’s Disease Prediction,The First International Conference On Artificial Intelligence, Smart Technologies And Communications – AISTC’2025,Univ de Chelef( Algeria)

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

BILAL Attallah , , (2025-02-06), 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, Science, Engineering and Technology

Enhanced COVID-19 detection in CT image using preprocessed CNN

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BILAL Attallah , ,(2024-12-03), Enhanced COVID-19 detection in CT image using preprocessed CNN,the first National Conference on Artificial Intelligence and its Applications (NCAIA2024),Mentouri Constantine

Automated brain tumor classification using fine-tuned efficient net model

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BILAL Attallah , ,(2024-12-03), Automated brain tumor classification using fine-tuned efficient net model,the first National Conference on Artificial Intelligence and its Applications (NCAIA2024),Mentouri Constantine

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

The increasing prevalence of crime, piracy, and security issues across various sectors underscores the necessity for dependable identity verification systems. Conventional security solutions, typically dependent on pre-existing data or token-based access, face challenges in differentiating between authorized individuals and impostors. This study investigates deep-learningdriven palmprint recognition systems as a superior option, utilizing the uniqueness, security, ease of use, and non-invasiveness of the palmprint modality.Our approach focuses on optimizing deep learning models for feature extraction, utilizing both singleinstance and multiple-instance learning techniques. The system consists of two parts: Initially, three convolutional neural network models—VGG16, VGG19, and MobileNetV2—are employed for feature extraction and classification. Preliminary results indicate the application of a feature fusion technique to combine features from both left and right palmprints, thereby establishing our multiple-instance system.Experimental evaluations on the IITD palmprint database illustrate the efficacy of this method, achieving a 98.50% accuracy using the multiple-instance strategy, highlighting its superiority compared to current techniques. Index Terms—One instance, Multiple instances, Recognition system, Convolution Neural Network, Palmprint.

Citation

BILAL Attallah , ,(2024-11-24), Multiple-Instance Palmprint Recognition System Using Deep Neural Networks,2nd National Conference on Electronics, Electrical Engineering Telecommunications, and Computer Vision (C3ETCV24),Mila_Algerie

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

BILAL Attallah , ,(2024-11-17), A Robust Convolutional Neural Network for Iris Recognition System,The First National Conference for Applied Sciences and Engineering (NCASE-24),ENSTA, Algérie

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

BILAL Attallah , , (2024-11-07), MRI-based brain tumor ensemble classification using two stage score level fusion and CNN models, Egyptian Informatics Journal, Vol:28, Issue:122024, pages:100564, sciencedirect

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

BILAL Attallah , , (2024-06-27), Two-stage deep learning classification for diabetic retinopathy using gradient weighted class activation mapping, Automatika, Vol:652024, Issue:3, pages:1284-1299, Taylor and francis

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

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

BILAL Attallah , , (2024-02-05), Gabor, LBP, and BSIF features: Which is more appropriate for finger-knuckles-print recognition?, Przegląd Elektrotechniczny, Vol:92024, Issue:62, pages:62, SCOPUS

Ensemble Learning VS Convolutional Neural Networks for Multiclass Brain Tumor Classification of MRI Images

Citation

BILAL Attallah , ,(2023-12-03), ICSTEM,ICSTEM,ISTANBUL

Deep lerning for biometric and biomedical application

Citation

BILAL Attallah , ,(2023-10-23), Deep lerning for biometric and biomedical application,Deep lerning for biometric and biomedical application,kualalumbur

Diabetes mellitus is a leading cause of diabetic retinopathy (DR), which results in retinal lesions and vision impairment. Untreated DR can lead to blindness, highlighting the need for early diagnosis and treatment. Unfortunately, DR has no cure, and treatments only help to preserve vision. Traditional manual diagnosis of DR retina fundus images by ophthalmologists is time-consuming, costly, and prone to errors. Computer-aided diagnosis methods, such as deep learning, have emerged as popular methods for improving diagnosis and reducing errors. Over the past decade, Convolutional Neural Networks (CNNs) have been shown to perform very well in medical image analysis due to their high ability to extract local features from images. Convolutional neural networks (CNNs) have shown great success in the processing of medical images, including DR color fundus images. In this paper, we proposed a multi-level fine-tuned deep learning based approach for the classification of diabetic retinopathy using three different pre-trained models including: DenseNet121, MobileNetV2, and Xception. The results are provided as classification accuracy, loss metrics, and the performance is compared with state-of-the-art works. The results indicates that the proposed Xception network surpassed its peers’ models as well as state-of-the-art methods by achieving the highest accuracy of 97.95% in binary classification of DR images.

Citation

BILAL Attallah , , (2023-06-15), A multi-level fine-tuned deep learning based approach for binary classification of diabetic retinopathy, Chemometrics and Intelligent Laboratory Systems, Vol:237, Issue:, pages:104820, sciencedirect

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.

Citation

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

Ear recognition using ensemble of deep learning and machine learning

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BILAL Attallah , ,(2022), Ear recognition using ensemble of deep learning and machine learning,ICCTA2022,Alexendrie-Egypt

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

Citation

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

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.

Citation

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.

Citation

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

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.

Citation

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)

In the contemporary period, significant attention has been focused on the prospects of innovative personal recognition methods based on palm-print biometrics. However, diminished local consistency and interference from noise are only some of the obstacles that hinder the most common methods of palm-print imaging such as the grey texture and other low-level of the palm. Nevertheless, the development of the process and tackling of the obstacles faced have a potential solution in the form of high-level characteristic imaging for palm-print identification. In this study, Zernike moments are used for acquiring superpixel features that are spiral scanned images, which is an innovative recognition method. By using the extreme learning machine, the inter- and intra-similarities of the palm-print feature maps are determined. Our experiments yield good results with an accuracy rate of 97.52 and an equal error rate of 1.47% on the palm-print PolyU database.

Citation

BILAL Attallah , , (2019), Superpixel-based Zernike moments for palm-print recognition, International Journal of Electronic Security and Digital Forensics, Vol:11, Issue:4, pages:420 - 433, INDERSCIENCE Publisher

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.

Citation

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.

Citation

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

Because of their high recognition rates, coding-based approaches that use multispectral palmprint images have become one of the most popular palmprint recognition methods. This paper describes a new multispectral palmprint recognition method that aims to further improve the performance of coding-based approaches by focusing on the local binary pattern (LBP) filters and spiral moments features. The final feature map is derived through a staged process of creating a composite of spiral and LBP features by fusing them together and passing the features through the minimum redundancy maximum relevance transformers. Using Hamming distances, the inter- and intra-similarities of the palmprint feature maps are determined. The experimental technique was evaluated using the available data on the IITD, MSPolyU and PolyU PPDB databases. The results indicate that the method achieved high levels of accuracy in the identification and verification modes. Furthermore, this method outperforms the existing advanced techniques.

Citation

BILAL Attallah , Amina Serir, Youssef Chahir, , (2019), Feature extraction in palmprint recognition using spiral of moment skewness and kurtosis algorithm, Pattern Analysis and Applications, Vol:22, Issue:3, pages:1197–1205, Springer Nature

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.

Citation

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

A new palmprint recognition system is presented here. The method of extracting and evaluating textural feature vectors from palmprint images is tested on the PolyU database. Furthermore, this method is compared against other approaches described in the literature that are founded on binary pattern descriptors combined with spiral-feature extraction. This novel system of palmprint recognition was evaluated for its collision test performance, precision, recall, F-score and accuracy. The results indicate the method is sound and comparable to others already in use.

Citation

BILAL Attallah , Youssef Chahir, Amina Serir, ,(2018), Geometrical Local Image Descriptors for Palmprint Recognition,ICISP 2018,Cherbourg-France

La biométrie est une technique globale visant la reconnaissance automatique des individus à partir de leurs caractéristiques physiologiques et/ou comportementales. Dans cette thèse, nous abordons plusieurs points importants concernant la biométrie bimodale. Nous nous proposons de réaliser un système d’authentification automatique à partir de la paume de la main et l'iris de l'œil. Le système proposé, doté de plusieurs modules, tire avantage de différents procédés pour réduire les taux de fausses acceptations et de faux rejets. Après avoir d’abord dressé un état de l’art des différents systèmes biométriques mono-modaux, nous faisons le lien entre les bases de données existantes, la sélection de caractéristiques pertinentes de dimensionsréduites pour identifier l'iris ou l'empreinte palmaire ainsi que leur fusion bimodale. En première partie, nous présentons nos différentes contributions pour l’analyse d’empreinte palmaire. Nosapproches consistent à combinerdesdescripteurs de texture locaux (tels que LBP, BSIF, LPQ, Fusion)etdes descripteurspar ondelettes (Gabor,Haar) à différents niveaux, pour l'extraction des lignes de la paume de la main. Dans notre étude, nous mettons en évidence les capacités de ces transformées à caractériser les textures oscillatoires ainsi que les courbures en vue de l'extraction de signatures biométriques robustes.Nous proposons également une méthode de caractérisation basée sur un parcours spiral des moments statistiques (moyenne, variance, asymétrie, aplatissement). En outre, nous avons affiné la caractérisation par la fusion de descripteurs de texture, suivi par une sélection des caractéristiques en exploitant les deux transforméesACP et mRMR. En seconde partie, nous abordonsl'analyse de l’iris de l'œil.Nous proposons de combiner l'approche de Daugman avec une analyse multi-échelle (multi-résolution et multi-directionnelle) afin de mieux caractériser les structures radiales de l'iris et surmonter les problèmes inhérents à l'acquisition (présence de lentilles, fermeture partielle des paupières, changement de contraste) et à la mise en correspondance. Dans la dernière partie, la fusion et la sélection des caractéristiques signatures biométriques issues des deux modalités (Iris et empreinte palmaire) ainsi que des analyses statistiques à grande échelle des scores de similarité provenant de chaque modalité, ont permis une connaissance approfondie des scores issus des systèmes biométriques étudiés Nous avons utilisé les deuxclassifieurs (KNN, ELM)etleurs performances ont été comparées aux méthodes existantes. Les résultats trouvés ontmontré l’efficacité des algorithmes proposés en termes de précision.

Citation

BILALAttallah , ,(2018); Réalisation d’un système d’authentification automatique bimodal par l’iris et la paume de la main,USTHB,

Palmprint recognition systems are dependent on feature extraction. A method of feature extraction using higher discrimination information was developed to characterize palmprint images. In this method, two individual feature extraction techniques are applied to a discrete wavelet transform of a palmprint image, and their outputs are fused. The two techniques used in the fusion are the histogram of gradient and the binarized statistical image features. They are then evaluated using an extreme learning machine classifier before selecting a feature based on principal component analysis. Three palmprint databases, the Hong Kong Polytechnic University (PolyU) Multispectral Palmprint Database, Hong Kong PolyU Palmprint Database II, and the Delhi Touchless (IIDT) Palmprint Database, are used in this study. The study shows that our method effectively identifies and verifies palmprints and outperforms other methods based on feature extraction.

Citation

BILAL Attallah , , (2018), Histogram of gradient and binarized statistical image features of wavelet subband-based palmprint features extraction, Journal of Electronic Imaging, Vol:26, Issue:6, pages:1017-9909, SPIE

A better quality of an image can be achieved through iterative image reconstruction for positron emission tomography (PET) as it employs spatial regularisation that minimises the difference of image intensity among adjacent pixels. In this paper, the Bayesian inference rule is applied to devise a novel approach to address the ill-posed inverse problem associated with the iterative maximum-likelihood Expectation-Maximisation (MLEM) algorithm by proposing a regularised constraint probability model. The proposed algorithm is more robust than the standard MLEM and in background noise removal with preserving edges to suppress the out of focus slice blur, which is the existent image artefact. The quality measurements and visual inspections show a significant improvement in image quality compared to conventional MLEM and the state-of-the-art regularised algorithms.

Citation

ABDELWAHHAB BOUDJELAL , BILAL Attallah , Zoubeida Messali, , (2018), PET image reconstruction based on Bayesian inference regularised maximum likelihood expectation maximisation (MLEM) method, International Journal of Biomedical Engineering and Technology, Vol:24, Issue:7, pages:337 - 354, inderscience

Contexte Il y a eu des progrès considérables dans l'instrumentation de mesure de données et les méthodes informatiques permettant de générer des images des données de TEP mesurées. Ces méthodes informatiques ont été développées pour résoudre le problème inverse, aussi appelé problème de « reconstruction de l'image à partir des projections ». But Dans cet article, les auteurs proposent un algorithme modifié pour la technique de reconstruction algébrique simultanée (SART), de façon à améliorer la qualité de la reconstruction de l'image en incorporant la minimisation de la variation totale (TV) dans l'algorithme itératif de SART. Méthodologie L'algorithme SART met à jour l'image estimative en faisant une projection avant de l'image sur l'espace du sinogramme. La différence entre le sinogramme estimé et le sinogramme donné est ensuite rétroprojetée sur le domaine de l'image. Cette différence est ensuite soustraite de l'image initiale pour obtenir une image corrigée. La minimisation rapide de la variation totale (FTV) est appliquée à l'image obtenue dans l’étape SART. La deuxième étape est le résultat obtenu de la mise à jour FTV précédente. Les étapes de SART et de minimisation FTV sont conduites de façon itérative, en alternance. Cinquante itérations ont été appliquées à l'algorithme SART utilisé dans chacune des méthodes fondées sur la régularisation. En plus de l'algorithme SART conventionnel, le lissage spatial a été utilisé pour améliorer la qualité de l'image. Toutes les images ont été produites en format 128 x 128 pixels. Résultats L'algorithme proposé a préservé les bordures avec succès. Un examen détaillé révèle que les algorithmes de reconstruction étaient différents; par exemple, l'algorithme SART et l'algorithme SART-FTV proposé ont préservé efficacement les bordures chaudes des lésions, tandis que les artefacts et les déviations étaient plus susceptibles d'apparaître dans l'algorithme ART que dans les autres algorithmes. Conclusion En comparaison de l'algorithme SART standard l'algorithme proposé réussit mieux à éliminer le bruit ambiant tout en préservant les bordures pour supprimer les artefacts existants. Les mesures de qualité et l'inspection visuelle montrent une amélioration significative de la qualité de l'image comparativement à l'algorithme SART traditionnel et à l'algorithme de technique de reconstruction algébrique (ART).

Citation

ABDELWAHHAB BOUDJELAL , BILAL Attallah , ZoubeidaMessali, AbderrahimElmoataz, , (2018), Improved Simultaneous Algebraic Reconstruction Technique Algorithm for Positron-Emission Tomography Image Reconstruction via Minimizing the Fast Total Variation, Journal of Medical Imaging and Radiation Sciences, Vol:48, Issue:4, pages:385-393, ELSEVIER