Detection and classification of gastrointestinal diseases using deep learning techniques

This document presents a research thesis for the Master in Computer Science (MCCi) degree at Tecnologico de Monterrey. Cancer is a pathological situation in which old or abnormal cells do not die when they should. Even though there are different cancer types, the incidence of colorectal cancer position it as the third most common one worldwide. Endoscopy is the primary diagnostic tool used to manage gastrointestinal (GI) tract malignancies, however, it is a time consuming and subjective process based on the experience of the clinician. Previous work has been done leveraging the use of artificial intelligence methods for polyps detection, instrument tracking and segmentation of gastric ulcers. This work is focused on the detec- tion and classification of gastrointestinal diseases. This thesis proposal seeks to implement a knowledge distillation framework with class-aware loss for endoscopic disease detection in the upper and lower part of the gastrointestinal tract. Relevant features will be extracted from endoscopic images to feed and train a deep learning-based object detection model. The method is evaluated using standard computer vision metrics: IoU and mAP25, mAP50, mAP75, mAP25:75. This proposal outperforms state-of-the-art methods and its vanilla version, which means that it has the potential to be an auxiliary quantitative tool to reduce high-missed de- tection rates in endoscopic procedures.