Gastrointestinal Mucosal Problems Classification with Deep Learning
This work addresses early diagnosis of gastrointestinal issues to prevent cancers, but it is incremental as it applies existing deep learning methods to a medical imaging task.
The paper tackled the classification of gastrointestinal mucosal changes and anatomical landmarks into 8 classes using deep learning, achieving 93% accuracy on test images and applying the model to real endoscopy and colonoscopy videos.
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.