TAWFIQ Beghriche

Filali Sabir , Salem Abdelhamid

Breast cancer classification using machine learning methods

Djerarda Imad Eddine

Real-Time Driver Safety System: Detection of Drowsiness and Distraction Using Artificial Intelligence

In this paper, we propose a new strategy to exploit the advantages of Deep Neural Network-based architectures for brain tumor classification using MRI images for a better diagnosis. This was achieved by analyzing and evaluating pre-trained models on three different datasets. To better design the optimal architecture for solving the classification of brain tumor using MRIs, we have conducted extensive experiment-based analysis on how different layers of Convolutional Neural Network (CNN) process the inputs. Four distinct architectures are then built, each with its specific hyperparameters and layers. The images are fed into the convolutional layers for feature extraction followed by a softmax function before applying the classification process. An extensive experimental study carried out clearly demonstrates that our novel CNN-based classification approach achieves state-of-the-art accuracy, precision, recall and an F1-score of 99.76% 99.64% 99.62% and 99.64%, respectively. Also, a higher performance in terms of Micro-Avg Matthew correlation coefficient (MCC) of 0.929 is achieved. This exceptional performance is achieved thanks to the new proposed model’s architecture. Indeed, unlike conventional methods, that often rely on complex transfer learning models or hybrid architectures, our approach utilizes a custom and non-hybrid scheme. Consequently, this streamlined architecture offers a significant advantage of being remarkably lightweight, enabling efficient operation on resource-constrained computing systems.

Citation

Tawfiq Beghriche , , (2024-07-12), Customized CNN for Multi-Class Classification of Brain Tumor Based on MRI Images, Arabian Journal for Science and Engineering, Vol:49, Issue:2024, pages:16903–16918, Springer

Breast cancer (BC) is one of the most fatal forms of cancer, making it a significant contributor to mortality rates worldwide. Early detection and timely treatment of breast cancer are crucial in reducing its mortality rate. To ensure a healthy lifestyle, it is essential to develop systems that can accurately diagnose breast cancer. Recent advances in modern computing and information technologies have enabled significant progress in the early detection and prediction of diseases within healthcare systems. This study proposes a method for precise and automatic breast cancer prediction using deep-modified transfer learning-based Convolutional Neural Networks (CNNs). The CNN architectures employed include ResNet50, MobileNetV2, DenseNet121, and Xception, which serve as feature extractors to capture the most relevant features of breast Ultrasound images (BUSI). These extracted features are then accurately classified as benign or malignant using various high-performance classifiers, including Support Vector Machine (SVM), K-Nearest Neighbors (KNN), XGBoost, and Softmax. The experimental results demonstrate that the proposed deep modified DenseNet121 network with the Softmax classifier outperformed other models and existing techniques. This latter achieved remarkable performance metrics, including an accuracy of 95.34%, a precision of 90.90%, and an F1 score of 93.02%. These results highlight the effectiveness of our approach in enhancing the accuracy of breast cancer prediction. The superior performance of the proposed method provides significant improvements in decision-making speed and reduces the time, effort, and laboratory resources required for healthcare services. Consequently, this method has the potential to significantly enhance early diagnosis and enable more tailored treatment plans, ultimately contributing to better patient outcomes and reducing the overall mortality rates associated with breast cancer.

Citation

Tawfiq Beghriche , , (2024-07-02), Deep-modified transfer learning-based CNN networks for enhanced breast cancer prediction, STUDIES IN ENGINEERING AND EXACT SCIENCES, Vol:5, Issue:2, pages:e5383, Studies Publicações Ltda

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

Tawfiq Beghriche , ,(2024-06-15), Enhancing Diabetic Retinopathy Detection Using a Hybrid Framework Integrating Machine Learning Classifiers and Advanced Feature Extraction Techniques,The 3rd International Conference on Frontiers in Academic Research.,Konya/Turkey

Water distribution networks (WDNs) are important infrastructures that provide water to communities. However, they do absorb, causing large amounts of water loss and damage to infrastructure. The methods of detecting and reporting leaks in WDNs are usually laborious and time-consuming. Recently, machine learning has demonstrated its ability to improve the efficiency and accuracy of leak detection methods. In this paper, we have proposed the use of pressure transducers due to their high accuracy and resistance to ambient noise compared to those available in the international market. We fabricated a new 100 m long PEHD sample pipe with a diameter of 40 mm, equipped with two transducers. In addition, we used the purchase dispositif of the DSPACE commercial research purchase order, specifically the MicroLabBox model. In this case the collected Data were processed, to ensure a complete picture of the leak signals. Various ML algorithms, including Support Vector Machines (SVM), Naïve Bayes (NB) and XGBoost, have been analyzed to classify the collected dataset into leak our no_leak classes accurately Overall, the obtained results show that XGBoost has an optimal performance of 90.91%. had the highest accuracy, outperforming other algorithms such as SVM (77.27%) and NB (68.18%) thus demonstrating the potential of ML methods to improve flows in WDNs.

Citation

Tawfiq Beghriche , ,(2024-06-15), Effective Leak Detection in Water Distribution Networks using Pressure Transducers and Machine Learning,The 3rd International Conference on Frontiers in Academic Research.,Konya/Turkey

Diabetes is a chronic metabolic disorder that affects an estimated 463 million people worldwide. Aiming to improve the treatment of people with diabetes, digital health has been widely adopted in recent years and generated a huge amount of data that could be used for further management of this chronic disease. Taking advantage of this, approaches that use artificial intelligence and specifically deep learning, an emerging type of machine learning, have been widely adopted with promising results. Therefore, several works have been done in diabetes detection using machine learning according to importance of diabetes. Significant efforts in diabetes prediction were highlighted here.

Citation

Tawfiq Beghriche , ,(2023-04-17), Machine and deep learning techniques for diabetes prediction: review,الذكاء الاصطناعي في خدمة المجتمع,Faculté de Technologie, Université Mohamed Boudiaf-M'sila, Algeria

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

Tawfiq Beghriche , , (2023-04-12), A multi-level fine-tuned deep learning based approach for binary classification of diabetic retinopathy, Chemometrics and Intelligent Laboratory Systems, Vol:273, Issue:, pages:8, ELSEVIER

Machine and deep learning techniques for diabetes prediction: Review

Citation

Tawfiq Beghriche , ,(2022), Machine and deep learning techniques for diabetes prediction: Review,Journée Doctoral en Electronique 2022 (JDE 2022),Faculté de Technologie, Université Mohamed Boudiaf-M'sila

Breast cancer has become a major leading cause of death and incapacity worldwide. Recently, breast cancer is being responsible for a huge number of deaths of the female gender. In this study, we have implemented the Twin-Support Vector Machine (TW-SVM) to illustrate the power of machine learning techniques. TW-SVM is a recently developed algorithm and yet it is very powerful. For performance measurement, a competitive comparison between the proposed TW-SVM and SVM classifiers has been done based on the WDBC dataset. The results showed that TW-SVM can provide promising performance rates. It outperformed the SVM algorithm as well as other existing works by achieving the highest accuracy of 99.11% for predicting the considered disease.

Citation

Tawfiq Beghriche , ,(2022), Performance Analysis of Twin-Support Vector Machine in Breast Cancer Prediction,The 2022 International Conference of Advanced Technology in Electronic and Electrical Engineering (ICATEEE),Faculty of Technology, University Mohamed Boudiaf of M'sila-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.

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