A machine learning approach for colorectal polyp detection, density analysis, and Cancer risk prediction using CVS-LBP-MRF model

Colon cancer ranks as the second leading cause of cancer-related mortality globally, with incidence rates contingent upon the cancer's progression through various stages. The disease typically initiates with the formation of small, benign cellular masses known as polyps. The early identification and removal of these polyps are crucial to prevent their progression into malignant tumors. Colonoscopy serves as a standard medical procedure for polyp detection, enabling clinicians to distinguish polyps based on their morphological characteristics. In this study, we propose a novel approach for automatic polyp analysis utilizing a saliency-based visual technique. Our method employs the CVS-LBP-MRF algorithm, which integrates a biologically inspired visual saliency model with Local Binary Patterns (LBP) features and a Markov Random Field (MRF) framework. This combination aids in the effective extraction of LBP features from sequences of colonoscopy video frames, focusing on the identification of polyp areas. The algorithm calculates the saliency and density of polyp regions, leveraging the strengths of the integrated methodologies to enhance detection accuracy. We validate our approach through extensive testing on a diverse set of colonoscopy videos, demonstrating its efficacy in distinguishing polyps from surrounding tissue. The results indicate that our saliency-based model significantly improves the automatic detection and analysis of polyps, thus bolstering the diagnostic capabilities of colonoscopy. By streamlining the identification process, this method has the potential to facilitate earlier interventions, thereby reducing the incidence of colon cancer progression. Future work will aim to refine the algorithm further, incorporate real-time analysis, and enhance its application in clinical settings, ultimately contributing to better outcomes in colon cancer screening and treatment.