Traitement des images médicales par Deep Learning : segmentation des polypes colorectaux pour l’aide au diagnostic

Abstract

Colorectal cancer is one of the most pressing global public health challenges, with nearly 1.9 million new cases diagnosed annually. Colonoscopy remains the gold standard for its preven- tion; however, polyp miss rates ranging from 22% to 40% persist due to the high morphological variability of lesions and operator fatigue. This thesis presents CANet+ (Colorectal Attention Network Plus), an encoder-decoder Deep Learning architecture combining a ConvNeXt-Tiny en- coder, an Attention U-Net decoder, CBAM attention modules, an ASPP module for multi-scale context modeling, and a deep supervision mechanism.Evaluated on three standard benchmarks Kvasir-SEG, CVC-ClinicDB, and CVC-ColonDB using a four-fold cross-validation protocol, CANet+ achieves Dice scores between 0.92 and 0.96 and IoU values between 0.87 and 0.92, demonstrating competitive performance against methods. These results open concrete prospects for integrating Deep Learning based computer aided diagnosis and segmentation systems into real world clinical endoscopic practice.