Analysis of Realtime Polyp Frame Detection and Polyp Segmentation by Activating Maxout Network Mechanism with Deep Learning

The detection and segmentation of polyps in real time for the purpose of colorectal cancer screening is the focus of this research study, which studies the use of deep learning and the Maxout Network approach. We were able to overcome the limitations of traditional methods and develop a dependable system for the automated and objective identification of polyps by making use of the Kvasir-SEG dataset. It has been shown that finding polyps early on may have an influence on reducing the rate at which cancer spreads. An analysis of comparative methods that is on the leading-edge sheds light on the benefits of our methodology. Our study demonstrates the promise of deep learning for real-time polyp recognition; nevertheless, there is still much more work to be done on concerns relating to generalizability and computational complexity. The many different datasets, their interpretability, and the regulatory issues for potential applications in the therapeutic realm need to be the primary focuses of future research.