Diagnosis of celiac disease and environmental enteropathy on biopsy images using color balancing on convolutional neural networks

Authors

Kamran Kowsari, University of Virginia, Charlottesville, USA.
Rasoul Sali, University of Virginia, Charlottesville, USA.
Marium N. Khan, University of Virginia, Charlottesville, USA.
William Adorno, University of Virginia, Charlottesville, USA.
Syed Asad Ali, Aga Khan UniversityFollow
Sean R. Moore, University of Virginia, Charlottesville, USA.
Beatrice C. Amadi, University of Zambia, School of Medicine, Lusaka, Zambia
Paul Kelly, University of Zambia, School of Medicine, Lusaka, Zambia
Sana Syed, University of Virginia, Charlottesville, USA.
Donald E. Brown, University of Virginia, Charlottesville, USA.

Document Type

Conference Paper

Department

Women and Child Health; Paediatrics and Child Health

Abstract

Celiac Disease (CD) and Environmental Enteropathy (EE) are common causes of malnutrition and adversely impact normal childhood development. CD is an autoimmune disorder that is prevalent worldwide and is caused by an increased sensitivity to gluten. Gluten exposure destructs the small intestinal epithelial barrier, resulting in nutrient mal-absorption and childhood under-nutrition. EE also results in barrier dysfunction but is thought to be caused by an increased vulnerability to infections. EE has been implicated as the predominant cause of under-nutrition, oral vaccine failure, and impaired cognitive development in low-and-middle-income countries. Both conditions require a tissue biopsy for diagnosis, and a major challenge of interpreting clinical biopsy images to differentiate between these gastrointestinal diseases is striking histopathologic overlap between them. In the current study, we propose a convolutional neural network (CNN) to classify duodenal biopsy images from subjects with CD, EE, and healthy controls. We evaluated the performance of our proposed model using a large cohort containing 1000 biopsy images. Our evaluations show that the proposed model achieves an area under ROC of 0.99, 1.00, and 0.97 for CD, EE, and healthy controls, respectively. These results demonstrate the discriminative power of the proposed model in duodenal biopsies classification.

Comments

Volume, and issue are not provided by the author/publisher

Publication (Name of Journal)

Proceedings of the Future Technologies Conference

Recommended Citation

Kowsari, K., Sali, R., Khan, M. N., Adorno, W., Ali, S., Moore, S. R., Amadi, B. C., Kelly, P., Syed, S., Brown, D. E. (2019). Diagnosis of celiac disease and environmental enteropathy on biopsy images using color balancing on convolutional neural networks. Proceedings of the Future Technologies Conference, 750-765.
Available at: https://ecommons.aku.edu/pakistan_fhs_mc_women_childhealth_paediatr/863