Adaptive DRL Specular Reflection Removal for Enhanced Polyps Detection

Abstract

Artificial intelligence (AI) applications in colonoscopy have shown promise in terms of improving disease detection and classification, such as polyp detection. However, specular reflections from the camera flash might have a detrimental influence on the inference process. We propose an adaptive deep reinforcement learning (DRL) model to enhance pre-trained polyp identification algorithms to eliminate specular reflections from colonoscopy video frames. The DRL model is trained to detect and reduce specular reflections in colonoscopy pictures while maintaining key characteristics. We test the feasibility of our DRL model by using it as a pre-processing step for cutting-edge polyp identification methods. The findings show that employing the DRL model to reduce specular reflections improves detection accuracy by 14.3% compared to the baseline polyp detection models. Our adaptive DRL technique successfully removes the reflection effect, causing inference performance issues in colonoscopy video processing. This proposed method opens the door to more accurate AI-assisted polyp identification during colon cancer assessment.