Colorectal Abnormalities Detection And Classification Using Deep Learning Techniques

Résumé: This dissertation aims to present deep learning techniques, specifically Convolutional Neural Networks (CNNs), for the detection and classification of colorectal polyps in medical images. Colorectal cancer is a significant global health concern, and early detection of polyps is crucial for effective treatment and prevention. The research focuses on developing a CNN-based model to analyze colonoscopy images and accurately identify regions containing polyps. Various CNN architectures and training strategies are investigated, including transfer learning and fine-tuning, to optimize the model's performance. The model is evaluated on the CVC-ClinicDB dataset, and the results demonstrate high accuracy, precision, sensitivity, and specificity in polyp detection and classification. The research contributes to the advancement of computer-aided diagnosis tools for colorectal cancer screening and has the potential to improve patient outcomes through earlier detection and intervention.

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