Deep Learning Algorithms for Detection and Classification of Gastrointestinal Diseases

Mosleh Hmoud Al-Adhaileh, Ebrahim Mohammed Senan, Fawaz Waselallah Alsaade, Theyazn H. H Aldhyani, Nizar Alsharif, Ahmed Abdullah Alqarni, M. Irfan Uddin, Mohammed Y. Alzahrani, Elham D. Alzain, Mukti E. Jadhav and Muhammad Javaid

Complexity, 2021, vol. 2021, 1-12

Abstract: Currently, nearly two million patients die of gastrointestinal diseases worldwide. Video endoscopy is one of the latest technologies in the medical imaging field for the diagnosis of gastrointestinal diseases, such as stomach ulcers, bleeding, and polyps. Medical video endoscopy generates many images, so doctors need considerable time to follow up all the images. This creates a challenge for manual diagnosis and has encouraged investigations into computer-aided techniques to diagnose all the generated images in a short period and with high accuracy. The novelty of the proposed methodology lies in developing a system for diagnosis of gastrointestinal diseases. This paper introduces three networks, GoogleNet, ResNet-50, and AlexNet, which are based on deep learning and evaluates them for their potential in diagnosing a dataset of lower gastrointestinal diseases. All images are enhanced, and the noise is removed before they are inputted into the deep learning networks. The Kvasir dataset contains 5,000 images divided equally into five types of lower gastrointestinal diseases (dyed-lifted polyps, normal cecum, normal pylorus, polyps, and ulcerative colitis). In the classification stage, pretrained convolutional neural network (CNN) models are tuned by transferring learning to perform new tasks. The softmax activation function receives the deep feature vector and classifies the input images into five classes. All CNN models achieved superior results. AlexNet achieved an accuracy of 97%, sensitivity of 96.8%, specificity of 99.20%, and AUC of 99.98%.

Date: 2021
References: Add references at CitEc
Citations:

Downloads: (external link)
http://downloads.hindawi.com/journals/complexity/2021/6170416.pdf (application/pdf)
http://downloads.hindawi.com/journals/complexity/2021/6170416.xml (application/xml)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:hin:complx:6170416

DOI: 10.1155/2021/6170416

Access Statistics for this article

More articles in Complexity from Hindawi
Bibliographic data for series maintained by Mohamed Abdelhakeem ().