Ben Othmane Sami2025-02-092025-02-092024http://dspace.univ-khenchela.dz:4000/handle/123456789/7800In recent years, Artificial Intelligence (AI) has revolutionized numerous fields of scientific research by offering innovative solutions to complex challenges. The healthcare sector is no exception. Diabetes, a prevalent condition both globally and in Algeria, often leads to a severe complication known as diabetic retinopathy. This condition can blur or distort vision and is a major cause of blindness. Early detection of diabetic retinopathy is crucial for effective treatment. Recent advancements in AI, especially in deep learning, provide promising solutions for predicting, diagnosing, and managing various diseases in their early stages. In our master project, we develop a robust system for detecting the levels of diabetic retinopathy using Convolutional Neural Networks (CNNs) enhanced with specific adaptations to improve performance. Leveraging a pre-trained CNN model, we introduce tailored adjustments to parameters that optimize the network for our dataset and the particular nuances of DR detection. Furthermore, we implement multi-label classification to address the complex and overlapping features of diabetic retinopathy stages. This approach allows our model to capture and represent the intricate patterns in retinal images more effectively than traditional single-label methods. Our experimental results demonstrate that this combined strategy significantly enhances the accuracy and reliability of Diabetic Retinopathy (DR) level detection. This project emphasizes the Importance of Machine learning and Deep learning techniques in healthcare, offering a promising tool for clinicians in the fight against diabetic eye diseases. .enThesis