RAHMOUNI, Oualid / MAALEM, Abdelmouaaz / Encadré par BOUCHEMHA, Amel2024-09-122024-09-122024http//localhost:8080/jspui/handle/123456789/11851Diabetic Retinopathy (DR) is a frequent complication of diabetes mellitus that compromises retinal function in more than 50\% of type 2 diabetic patients. It occurs when the retina's blood vessels deteriorate. These altered vessels can dilate, leak fluid (plasma, lipids, and/or blood), and even clog, leaving part of the retina without blood flow. All these phenomena that occur as a result of diabetes can cause progressive damage to the structures of the eyeball, leading to a severe reduction in vision and even, without appropriate treatment, to blindness in the working age. In our work, we propose a framework for diabetic retinopathy detection based on retinal lesions using advanced deep learning. We use the U-Mamba architecture for retinal and blood vessel segmentation, achieving high F1 scores for various lesion types. Our Swin Transformer-based classification model, incorporating lesion segmentation masks, demonstrated exceptional performance across multiple datasets, with up to 97.75\% accuracy on the EyePACS dataset. This approach outperformed existing models across various datasets, showing promise for clinical DR diagnosis.frDiabetic Retinopathy Detection, Retinal Lesions Segmentation, Blood vessel segmentation, Deep Learning, Retina Image Analysis.Thesis