GastroEffNetV1- CNN based Automated detection of Gastrointestinal abnormalities from capsule endoscopy images

Abstract

Abstract Purpose: Gastrointestinal disorders are a class of prevalent disorders in the world. Capsule endoscopy is considered as an effective diagnostic modality for diagnosis of such gastrointestinal disorders. Aim: The work is to leverage an algorithm for automated classification of the gastrointestinal abnormalities using capsule endoscopy images using Deep learning algorithms. Method: In this method we proposed a deep learning architecture GastroEffNetV1 for automatic classification of the abnormalities in the capsule endoscopy images. The gastrointestinal abnormalities considered are ulcerative colitis, polyps and esophagitis. The curated dataset consists of 6000 images with ground truth labeling. A website was developed using the trained algorithm to execute automatic classification of the input image as either ulcerative colitis, polyp, esophagitis or as normal condition. Result: The classifier produced 99.15% validation accuracy, 0.0918 validation loss, 99.25% specificity and 99.25% sensitivity and 0.991 AUC. These results exceed that of the state-of-the-art systems. Conclusion: Hence the GastroEffNetV1 could be used to identify the different gastrointestinal abnormalities in the capsule endoscopy image which will in turn increase quality of healthcare.