Multi-Modal Detection in Colonoscopy: A Data-Driven Approach

Colorectal cancer is one of the leading causes of cancer-related deaths worldwide. Early detection of polyps is essential because removing them can prevent cancer development. Colonoscopy is commonly used to examine the colon, but detecting polyps in colonoscopy images can be challenging due to lighting variations, reflections, and complex tissue structures. This research proposes a deep learning based system that analyzes colonoscopy images and automatically detects the presence of polyps using a Convolutional Neural Network (CNN). The model learns visual patterns such as texture, shape, and color variations that distinguish polyp regions from normal colon tissue. Experimental results demonstrate reliable performance using metrics including accuracy, precision, recall, and F1-score. The study highlights the potential of artificial intelligence in assisting medical image analysis and improving early polyp detection.