Gastrointestinal Mucosal Problems Classification with Deep Learning

TL;DR

This paper applies transfer learning with CNNs to classify eight types of gastrointestinal mucosal changes and landmarks, achieving 93% accuracy and demonstrating potential for real endoscopy applications.

Contribution

It introduces a CNN-based transfer learning approach for classifying gastrointestinal mucosal problems, comparing multiple architectures and validating on real endoscopy videos.

Findings

Achieved 93% accuracy on test images.

Compared VGG, Inception, Xception, ResNet architectures.

Successfully classified problems in real endoscopy videos.

Abstract

Gastrointestinal mucosal changes can cause cancers after some years and early diagnosing them can be very useful to prevent cancers and early treatment. In this article, 8 classes of mucosal changes and anatomical landmarks including Polyps, Ulcerative Colitis, Esophagitis, Normal Z-Line, Normal Pylorus, Normal Cecum, Dyed Lifted Polyps, and Dyed Lifted Margin were predicted by deep learning. We used neural networks in this article. It is a black box artificial intelligence algorithm that works like a human neural system. In this article, Transfer Learning (TL) based on the Convolutional Neural Networks (CNNs), which is one of the well-known types of neural networks in image processing is used. We compared some famous CNN architecture including VGG, Inception, Xception, and ResNet. Our best model got 93% accuracy in test images. At last, we used our model in some real endoscopy and colonoscopy movies to classify problems.