Survey of Computer Vision and Machine Learning in Gastrointestinal Endoscopy

TL;DR

This survey reviews the application of computer vision and machine learning in gastrointestinal endoscopy, categorizing existing methods but primarily focusing on pre-deep learning approaches, serving as a foundational overview.

Contribution

It provides a comprehensive classification of early computer vision and machine learning techniques in GI endoscopy, highlighting the state of the field before deep learning advancements.

Findings

Classified 18 categories of methods

Highlights the gap in deep learning applications

Serves as a foundational reference for future research

Abstract

This paper attempts to provide the reader a place to begin studying the application of computer vision and machine learning to gastrointestinal (GI) endoscopy. They have been classified into 18 categories. It should be be noted by the reader that this is a review from pre-deep learning era. A lot of deep learning based applications have not been covered in this thesis.