Computer Aided Medical Image Analysis for Capsule Endoscopy using Multi-class Classifier

One-third of the world population suffers from diseases related to gastrointestinal (GI) tract. Capsule endoscopy is a non-sedative, non-invasive and patient-friendly technology to scan the entire GI tract. However, capsule endoscopy generates approximately 60000 images which make the diagnosis process time consuming and tiresome for physicians. Hence a computer-aided diagnosis system is a must. In this study, addresses a multi-class medical image analysis problem using image processing and machine learning techniques. This work proposes a system comprising preprocessing, feature extraction and classification of capsule endoscopy images for automatic detection of GI tract diseases. The system performs with an accuracy of 93% and precision of 91.9%.