Mohammed Elbachir Yahiaoui2026-10-062026-07-06https://dspace.univ-tebessa.dz/handle/123456789/523This thesis addresses the critical challenge of accurate brain cancer diagnosis while ensuring patient data privacy through the integration of deep learning (DL) and federated learning (FL). A novel 3D U- Net architecture with adaptive pooling techniques is developed, achieving superior tumour segmentation performance (Dice similarity coefficient >0.89) on BraTS datasets by effectively capturing heterogeneous tumour features. The model incorporates a Choquet integral-based fusion mechanism to enhance diagnostic precision by modeling complex feature interactions. To overcome data privacy barriers, a differentially private FL framework is implemented, enabling secure multi- institutional collaboration without raw data sharing. This approach maintains segmentation accuracy (DSC: 0.86–0.91) while complying with GDPR/HIPAA regulations through secure aggregation and differential privacy. The research culminates in a diagnostic platform prototype that integrates segmentation, visualization, and treatment planning tools, validated for clinical utility. Key contributions include: (1) State-of-the-art segmentation performance via optimized 3D pooling and Choquet fusion; (2) Privacy-preserving FL that matches centralized model accuracy; (3) An end- to-end platform supporting real-time clinician collaboration. Future work will extend this framework to broader oncological pathologies and enhance model interpretability for clinical adoption.enBrain tumour segmentationDeep learningFederated learningMedical image analysisPrivacy-preserving techniquesDiagnostic platformThesis