Duodenal biopsies classification and understanding using convolutional neural networks
Document Type
Conference Paper
Department
Paediatrics and Child Health; Women and Child Health
Abstract
Environmental Enteropathy (EE) and celiac disease (CD) are gastrointestinal conditions that adversely impact the growth of children. EE is prevalent in low- and middle-income countries, whereas as CD is prevalent worldwide. The histologic appearance of duodenal EE biopsies significantly overlaps with celiac enteropathy. We propose a convolutional neural network (ConvNet) to classify EE cases from Pakistani infants along with celiac and healthy controls from the United States. We also identified areas of biopsies that generate high activation values in the ConvNet model. The identified features helped in distinguishing EE and celiac from healthy intestinal tissues. This work advances the understanding of both diseases and provides a potential screening and diagnostic tool for practitioners.
Publication (Name of Journal)
AMIA Joint Summits on Translational Science proceedings
Recommended Citation
Boni, M. A.,
Syed, S.,
Ali, S.,
Moore, S. R.,
Brown, D. E.
(2019). Duodenal biopsies classification and understanding using convolutional neural networks. AMIA Joint Summits on Translational Science proceedings, 2019, 453-461.
Available at:
https://ecommons.aku.edu/pakistan_fhs_mc_women_childhealth_paediatr/875
Comments
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