Deep Learning-Based Pixel-Level Colonoscopy Analysis

Colorectal cancer (CRC) is a common form of cancer that affects the large intestine. CRC is one of the most severe and aggressive forms of cancer, and thus, early treatment and detection are essential. Early detection of CRC is primarily available through the detection of polyps through endoscopic imaging procedures. This method is labor-intensive and subject to human error. To circumvent these issues associated with human error and improve upon limitations associated with human detection, deep learning-based procedures have been developed and convolutional neural networks (CNNs) have been introduced for the automated detection and segmentation of polyps. Current problems associated with polyp segmentation with CNNs are overfitting, boundary pixel definitions, an inability to account for the different range of textures, sizes, and shapes with polyps, among other issues. With the ultimate goal of addressing these issues, we developed a multiscale segmentation network (MSSNet) designed specifically for polyps (Lewis and Cha, Sci Rep, 2023). In this paper, we conducted some additional case studies to investigate the performance of MSSNet. This dual model network surpasses state-of-the-art results (SOTA) and is evaluated using the CVC-ClinicDB dataset. The mean intersection-over-union (mIoU) and dice (mDice) score were 0.889 and 0.935, respectively.