Published Paper Details:

Colonoscopy is essential for detecting colorectal cancer (CRC) and pre-cancerous polyps, allowing for timely intervention and better patient care. Nonetheless, the manual analysis of colonoscopy images can be slow and prone to human mistakes, which increases the likelihood of overlooking polyps or making incorrect diagnoses. This study examines the use of deep learning techniques to automate the detection and classification of polyps in colonoscopy images. By employing convolutional neural networks (CNNs) and sophisticated image processing methods, the research seeks to improve the accuracy, efficiency, and dependability of colonoscopy analysis, aiding healthcare providers in diagnosing conditions related to the colon. The focus of this work is on preparing colonoscopy images, isolating significant regions, and extracting important features to train a deep learning model for classification purposes. The suggested system framework combines the segmentation and classification models to differentiate between normal and abnormal colon tissues. The method has been evaluated using a thorough dataset of colonoscopy images, showing significant enhancements in detection accuracy compared to traditional methods.