  Celiac Disease (CD) and Environmental Enteropathy (EE) are common causes of
malnutrition and adversely impact normal childhood development. 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 four diagnosis techniques for these diseases and address their
limitations and advantages. First, the diagnosis between CD, EE, and Normal
biopsies is considered, but the main challenge with this diagnosis technique is
the staining problem. The dataset used in this research is collected from
different centers with different staining standards. To solve this problem, we
use color balancing in order to train our model with a varying range of colors.
Random Multimodel Deep Learning (RMDL) architecture has been used as another
approach to mitigate the effects of the staining problem. RMDL combines
different architectures and structures of deep learning and the final output of
the model is based on the majority vote. CD is a chronic autoimmune disease
that affects the small intestine genetically predisposed children and adults.
Typically, CD rapidly progress from Marsh I to IIIa. Marsh III is sub-divided
into IIIa (partial villus atrophy), Marsh IIIb (subtotal villous atrophy), and
Marsh IIIc (total villus atrophy) to explain the spectrum of villus atrophy
along with crypt hypertrophy and increased intraepithelial lymphocytes. In the
second part of this study, we proposed two ways for diagnosing different stages
of CD. Finally, in the third part of this study, these two steps are combined
as Hierarchical Medical Image Classification (HMIC) to have a model to diagnose
the disease data hierarchically.
