Houari, AbdelmoghithBelhadj, Tahar Younes2026-10-262026-10-262026-06-22https://dspace.univ-tlemcen.dz/handle/112/27125Brain image classification and segmentation are challenging tasks in the field of medical image analysis. Most of the traditional approaches have largely treated these two tasks in isolation; however, in clinical practice, they are deeply connected to each other. The aim of this project is to propose and implement a hybrid model capable of performing brain tumor segmentation and grade classification simultaneously; the core of this proposed model integrates a custom-built Convolutional Neural Network (CNN) designed to extract rich spatial features from multi-modal MRI volumes with a Bidirectional Long Short-Term Memory (BiLSTM) network that captures the contextual relationships between consecutive brain slices. All experiments are conducted on the BraTS 2020 benchmark dataset, a widely adopted standard in the medical imaging community. To situate the proposed model's performance in a meaningful context, it is compared with two baselines: a ResNet34-LSTM and a DenseNet201-LSTM. The proposed model achieves a mean Dice score of 0.7326 across tumor sub-regions (Whole Tumor: 0.8862, Tumor Core: 0.8372, Enhancing Tumor: 0.7813) and an AUC of 0.9541 for glioma grading, with a Balanced Accuracy of 0.9130. This work demonstrates that spatial feature extraction and sequential context modeling are not competing paradigms but natural complements and that a carefully designed hybrid architecture can bring both tasks under one roof without sacrificing performance on either.enBrain tumor segmentationHGG/LGG classificationCNN-BiLSTMmulti-task learningBraTS 2020medical image analysisDeep learning.Thesis