Published Paper Details:

Finding colorectal polyps, which are precursors to colorectal cancer, requires a colonoscopy. The possibility of automated polyp recognition in colonoscopy pictures to help gastroenterologists improve the efficiency and accuracy of their diagnoses has drawn a lot of attention. In this work, we suggest a brand-new, two-step procedure for polyp identification in colonoscopy pictures. We use a deep learning-based method for initial polyp localization in the first stage. The goal of convolutional neural networks (CNNs) is to improve efficiency by reducing the computing burden by focusing exclusively on areas with probable polyps. CNNs are trained on a huge dataset of annotated colonoscopy images to recognize regions of interest likely to stage.. To improve the accuracy of polyp detection, we conduct a more thorough study of the regions of interest found in the first stage in the second stage. To further describe the discovered regions of interest, we use sophisticated image processing techniques like texture analysis, shape recognition, and context-aware feature extraction. By decreasing false positives and raising overall detection accuracy, this step improves polyp detection's specificity. We experimented with a wide collection of colonoscopy pictures, including instances with different polyp types, sizes, and textures, to assess the effectiveness of our technique. When compared to other methods, our technique shows promising results in terms of both sensitivity and specificity. Additionally, the suggested two-stage architecture demonstrates resilience to noise, changes in lighting, and other typical difficulties.. In summary, the suggested two-step approach combines the advantages of deep learning-based initial localization with cutting-edge image processing techniques for refined identification, providing an efficient means of automated polyp detection in colonoscopy pictures. By improving the efficiency and accuracy of colorectal polyp detection, the integration of such automated technologies into clinical practice may ultimately improve patient outcomes for colorectal cancer screening and diagnosis.