An Approach to Optimizing Machine Learning Models for the ‎Diagnosis of COVID-19 via Hyperparameter Optimization‎

The process of picking appropriate hyper-parameters for classification or prediction algorithms is a tough endeavor in the field of modeling. This selection is essential for the capacity for generalization and the performance of classifiers. Over the course of two tests, this article examines and evaluates the performance of five different Machine Learning (ML) algorithms: Support Vector Machine (SVM), AdaBoost, RandomForest, XGBoost, and DecisionTree. When it comes to training and testing, the first experiment makes use of the default settings, while the second experiment makes use of the GridSearch function to locate the most effective configurations. The tests make use of a dataset that was gathered from Albert Einstein Hospital in Sao Paulo, Brazil, and the dataset contains anonymous individuals that either have or do not have COVID-19. Usage of evaluation metrics includes things like accuracy, precision, recall, area under the curve (AUC), and F1-score. According to the findings, improving hyper-parameters results in an 18% improvement in recall

Transfer Learning Approach Leveraging EfficientNetV2L to Enhance ‎Skin Disease Prediction through Data Augmentation

Data imbalance is a common challenge in machine learning, particularly in medical image analysis tasks such as skin disease prediction. Melanoma, a type of skin cancer, is a prime example of a rare disease where the number of positive cases (minority class) is significantly smaller than the negative cases (majority class). This imbalance can lead to biased models that perform poorly in predicting rare diseases. Data augmentation techniques offer a solution by artificially increasing the number of minority class samples, thereby addressing the imbalance issue and improving the generalization of transfer learning models. In this paper, we focus on the application of data augmentation to balance the dataset for skin disease prediction. We specifically evaluate the performance of the Tuned EfficientNetV2L based classifier, a state-of-the-art model known for its efficiency and effectiveness in image classification tasks. Our experiments are conducted on a comprehensive collection of medical images and associated data of skin lesions sourced from various sources. These images represent diverse skin conditions, including both common and rare diseases, to ensure the robustness of our evaluation. To assess the performance of our approach, we employ various performance evaluation metrics such as accuracy, precision, and recall. These metrics provide insights into the classifier's ability to correctly classify skin lesions, especially rare diseases like melanoma

Advancements in Transfer Learning Models: A Robust Framework for ‎Precise COVID-19 Detection in X-Ray Images

A new coronavirus pandemic, known as COVID-19, first appeared in Wuhan, China, and caused a worldwide health emergency. The virus spread quickly and was characterised by common symptoms like fever, coughing, and respiratory discomfort. To address the urgent demand for effective diagnostic instruments, this research study presents a Deep Learning-based system designed for COVID-19 illness detection. Using six pre-trained models, the suggested diagnostic system makes use of the Transfer Learning technique to maximise performance by utilising experience. The Deep Learning system's performance evaluation confirms the Xception neural network's superior accuracy in identifying COVID-19 cases in the studied dataset. Notably, the system achieves commendable metrics, with an accuracy rate of 98% and a sensitivity rate of 100%

Parkinson’s diagnosis hybrid system based on deep learning classification with imbalanced dataset

Brain degeneration involves several neurological troubles such as Parkinson’s disease (PD). Since this neurodegenerative disorder has no known cure, early detection has a paramount role in improving the patient’s life. Research has shown that voice disorder is one of the first symptoms detected. The application of deep learning techniques to data extracted from voice allows the production of a diagnostic support system for the Parkinson’s disease detection. In this work, we adopted the synthetic minority oversampling technique (SMOTE) technique to solve the imbalanced class problems. We performed feature selection, relying on the Chi-square feature technique to choose the most significant attributes. We opted for three deep learning classifiers, which are long-short term memory (LSTM), bidirectional LSTM (Bi-LSTM), and deep-LSTM (D-LSTM). After tuning the parameters by selecting different options, the experiment results show that the D-LSTM technique outperformed the LSTM and Bi-LSTM ones. It yielded the best score for both the imbalanced original dataset and for the balanced dataset with accuracy scores of 94.87% and 97.44%, respectively

Parallel Algorithm for Brain Tissues Segmentation in T1-Weighted MR Images on 3D Reconfigurable Mesh Computer

In this paper, we propose a parallel algorithm for brain tissues segmentation from T1-weighted Magnetic Resonance Images (MRI) on Massively Parallel architecture named reconfigurable mesh computer (MCR), this brain tissues are already extracted using our method named Threshold Morphologic Brain Extraction method (TMBE)[1]. The use of this massively parallel architecture is introduced in order to improve the complexities of the corresponding algorithms. The image of size (M x N x K) to be processed must be stored on the RMC of the same size, one Voxel per Processing Element (PE). The proposed method consists in the brain tissues segmentation using parallel version of the modified fuzzy c-means MFCM [2], named PMFCM. This algorithm is directly applied on the extracted volume. The corresponding parallel program of the proposed algorithm is validated on a 3D Reconfigurable Mesh emulator [3]

Vers une accélération performante des applications de traitement d’images sur architectures parallèles

Les systèmes parallèles et distribués sont devenus, depuis quelques années, des incontournables issues pour le domaine du calcul de haute performance. Selon les problèmes et les contextes considérés, plusieurs architectures parallèles et techniques algorithmiques de distribution de données et de traitements sont apparus. Dans ce papier nous nous proposons, à travers une revue de littérature et un retour d’expérience, quelques aspects fondamentaux liés aux différents enjeux mis au cours de cette transformation de paradigme séquentiel-parallèle ainsi que les différentes contraintes auxquels la communauté technique et scientifique doit vaincre. L’accent est mis sur les applications et les algorithmes de traitement d’images accélérés via des architectures parallèles de type GPU. Une validation concrète, à travers une étude comparative de performances de trois algorithmes de classification floue appliqués à la segmentation d’images médicales, est présenté

Concatenation of Pre-Trained Convolutional Neural Networks for an Enhanced Corona Virus Screening Using Transfer Learning Technique

Coronavirus is the most prevalent coronavirus infection with respiratory symptoms such as fever; cough, dyspnea, pneumonia, and weariness being typical in the early stages. On the other hand, Coronavirus has a direct impact on the circulatory and respiratory systems as it causes a failure to some human organs or severe respiratory distress in extreme circumstances. Early diagnosis of Coronavirus is extremely important for the medical community to limit its spread. For large number of suspected cases, manual diagnostic methods based on the analysis of chest images are insufficient. Faced with this situation, artificial intelligence (AI) techniques have shown great potential in automatic diagnostic tasks. This paper aims at proposing a fast and precise medical diagnosis support system (MDSS) that can distinguish Coronavirus precisely in Chest-X-ray images. This MDSS uses a concatenation technique that aims to combine pre-trained convolutional neural networks (CNN) depend on the transfer learning (TL) technique to build a highly accurate model. The models enable storage and application of knowledge learned from a pre-trained CNN to a new task, viz., Coronavirus case detection. For this purpose, we employed the concatenation method to aggregate the performances of numerous pre-trained models to con-firm the reliability of the proposed method for identifying the patients with Coronavirus disease from X-ray images. The proposed system was trained on a dataset that included four classes: normal, viral-pneumonia, tuberculosis, and Coronavirus cases. Various general evaluation methods were used to evaluate the effectiveness of the proposed model. The first proposed model achieved an accuracy rate of 99.80% while the second model reached an accuracy of 99.71%

A Novel Explainable CNN Model for Screening COVID-19 on X-ray Images

Due to the rapid propagation characteristic of the Coronavirus (COVID-19) disease, manual diagnostic methods cannot handle the large number of infected individuals to prevent the spread of infection. Despite, new automated diagnostic methods have been brought on board, particularly methods based on artificial intelligence using different medical data such as X-ray imaging. Thoracic imaging, for example, produces several image types that can be processed and analyzed by machine and deep learning methods. X-ray imaging materials widely exist in most hospitals and health institutes since they are affordable compared to other imaging machines. Through this paper, we propose a novel Convolutional Neural Network (CNN) model (COV2Net) that can detect COVID-19 virus by analyzing the X-ray images of suspected patients. This model is trained on a dataset containing thousands of X-ray images collected from different sources. The model was tested and evaluated on an independent dataset. In order to approve the performance of the proposed model, three CNN models namely Mobile-Net, Residential Energy Services Network (Res-Net), and Visual Geometry Group 16 (VGG-16) have been implemented using transfer learning technique. This experiment consists of a multi-label classification task based on X-ray images for normal patients, patients infected by COVID-19 virus and other patients infected with pneumonia. This proposed model is empowered with Gradient-weighted Class Activation Mapping (Grad-CAM) and Grad-Cam++ techniques for a visual explanation and methodology debugging goal. The finding results show that the proposed model COV2Net outperforms the state-of-the-art methods

Formation et enseignement des mathématiques et des sciences : didactique, TIC et innovation pédagogique

International audienceLe Centre Régional des Métiers de l'Education et de la Formation Casablanca - Settat (Section d'El Jadida), les départements des Mathématiques et d’Informatique CRMEF Casablanca - Settat ont organisé la deuxième édition duColloque International CIFEM’2018 sur le thème : « Avenir de la formation et de l’enseignement des mathématiques et des sciences à l’ère du numérique» à El Jadida (Maroc), les 05 et 06 Avril 2018. Cet évènement montre la volonté des organisateurs de développer la qualité de l’enseignement des mathématiques au Maroc mais aussi en Afrique et la place importante que prend la formation dans ce développement. Cette manifestation est d’autant plus pertinente que comme l’indique l’argumentaire accompagnant l’appel à communications les résultats d’évaluations internationales ou nationales (TIMSS) sont inquiétants pour certains pays (le Maroc et la France notamment). L’amélioration des performances des élèves et des apprentissages est pour une part importante liée au développement de la formation des enseignants, ce colloque se propose donc non seulement de participer à la diffusion des recherches sur l’enseignement et la formation mais aussi d’enrichir la formation des enseignants par ces résultats.Dans ce but le colloque a permis de travailler cinq axes :Axe 1 : Formation et enseignement des mathématiques et des sciences : didactique et approches pédagogiquesAxe2 : TIC, formation, enseignement et innovation pédagogiqueAxe 3 : Collaboration et interférence des mathématiques et des sciences pour un développement mutuel.Axe 4 : Langage mathématique et scientifique et langue d’enseignementAxe 5 : Analyses de pratiques d’enseignemen