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Diagnosis and Analysis of Celiac Disease and Environmental Enteropathy on Biopsy Images using Deep Learning Approaches

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Document pages: 153 pages

Abstract: Celiac Disease (CD) and Environmental Enteropathy (EE) are common causes ofmalnutrition and adversely impact normal childhood development. Both conditionsrequire a tissue biopsy for diagnosis and a major challenge of interpretingclinical biopsy images to differentiate between these gastrointestinal diseasesis striking histopathologic overlap between them. In the current study, wepropose four diagnosis techniques for these diseases and address theirlimitations and advantages. First, the diagnosis between CD, EE, and Normalbiopsies is considered, but the main challenge with this diagnosis technique isthe staining problem. The dataset used in this research is collected fromdifferent centers with different staining standards. To solve this problem, weuse color balancing in order to train our model with a varying range of colors.Random Multimodel Deep Learning (RMDL) architecture has been used as anotherapproach to mitigate the effects of the staining problem. RMDL combinesdifferent architectures and structures of deep learning and the final output ofthe model is based on the majority vote. CD is a chronic autoimmune diseasethat affects the small intestine genetically predisposed children and adults.Typically, CD rapidly progress from Marsh I to IIIa. Marsh III is sub-dividedinto IIIa (partial villus atrophy), Marsh IIIb (subtotal villous atrophy), andMarsh IIIc (total villus atrophy) to explain the spectrum of villus atrophyalong with crypt hypertrophy and increased intraepithelial lymphocytes. In thesecond part of this study, we proposed two ways for diagnosing different stagesof CD. Finally, in the third part of this study, these two steps are combinedas Hierarchical Medical Image Classification (HMIC) to have a model to diagnosethe disease data hierarchically.

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