Abbes,Abd EnnourNahal, Selsabil2025-11-262025-11-262025-06-30https://dspace.univ-tlemcen.dz/handle/112/25302This work presents a system for automated analysis of white blood cells in microscopic blood smear images for the detection and classification of leukemia subtypes. We constituted and preprocessed a data game of over 18 000 labeled white blood cell images covering four categories of leukemia as well as healthy samples. With the help of feature-refined convolutional neuronal networks, we obtain high accuracy to distinguish subtle morphological differences between these subtypes. The classifier is then integrated into desktop, web and mobile applications, offering real-time inference and a user-friendly interface for clinicians. The experimental results show the robustness of the system in the face of coloration variations and its interest for an early diagnosis.enLeukemiaWhite Blood CellsBlood Smear Imageimage analysisConvolutional Neural NetworkDeep Learningdataset.Thesis