Comparative Analysis of U-Net and DeepLab for Automatic Polyp Segmentation in Colonoscopic Frames Using CVC-ClinicDB Dataset

Abstract

In the context of medical image segmentation, accurate polyp detection in colonoscopy videos is crucial for early colorectal disease diagnosis. This study compares U-Net and DeepLab deep learning models in automatically segmenting polyps using the CVC-ClinicDB dataset. The dataset comprises 612 images from 31 colonoscopy sequences with a resolution of 384×288.Our primary metric is Mean Intersection over Union (IoU), measuring the overlap between predicted and ground truth masks. We also evaluate Mean Dice Loss for comprehensive comparison. U-Net demonstrates superior performance, with a Mean IoU Score of 0.9897 and a low Mean Dice Loss of 0.0523, indicating consistent and accurate polyp segmentation. In contrast, DeepLab achieves a Mean IoU Score of 0.9676 and a slightly higher Mean Dice Loss of 0.0417, showing good results but being outperformed by U-Net.In conclusion, U-Net excels in automatic polyp segmentation, offering high accuracy and robustness. These findings advance computer-aided diagnosis for colorectal diseases, potentially enhancing early and precise polyp detection.