Enhancing Colorectal Lesion Segmentation Through Internal Feature Extraction and Computational Modeling Insights

Colorectal cancer (CRC) is a prevalent malignancy with significant social and healthcare implications, ranking among the top causes of cancer-related mortality worldwide. In this computational social systems context, we address the challenge of accurately segmenting CRC lesions from colonoscopy images, which is pivotal for early cancer detection and treatment. The complexity of intestinal environments and variations in medical expertise contribute to the high rate of undetected or misdiagnosed lesions, underscoring the need for advanced computational models. This study presents ColoSegNet, a novel self-supervised deep learning framework designed to enhance the accuracy and effectiveness of CRC diagnosis. By leveraging a comprehensive and annotated colorectal lesion segmentation dataset (CLSD), ColoSegNet incorporates a temporal correlation module to extract critical features from colonoscopy video frames, significantly improving the segmentation of colorectal lesions. Furthermore, ColoSegNet employs a masked autoencoder (MAE) module for self-supervised image reconstruction, preserving the original image integrity and facilitating precise segmentation. Comparative assessments against established models such as UNet, PraNet, and Deeplab V3 demonstrate ColoSegNet’s superior performance in detailed feature representation and overall segmentation accuracy. This research not only contributes to the field of medical imaging but also to computational social systems, by capturing inherent data patterns and integrating specialized modules for feature representation in a healthcare context. Our findings provide valuable insights into the early and accurate detection of CRC, a critical issue given the disease’s high incidence and mortality rates, and its impact on social systems.