For testing our performance, we use biopsy of the small bowel images that contain three categories in the parent level (Celiac Disease, Environmental Enteropathy, and histologically normal controls). HMIC uses stacks of deep learning models to give particular comprehension at each level of the clinical picture hierarchy. We performed a hierarchical classification using our Hierarchical Medical Image classification (HMIC) approach. This paper outlines an approach that is different from the current medical image classification tasks that view the issue as multi-class classification. As this field is explored, there are limitations to the performance of traditional supervised classifiers. Improved information processing methods for diagnosis and classification of digital medical images have shown to be successful via deep learning approaches.
Image classification is central to the big data revolution in medicine. 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. In the second part of this study, we proposed two ways for diagnosing different stages of CD. 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. Typically, CD rapidly progress from Marsh I to IIIa. CD is a chronic autoimmune disease that affects the small intestine genetically predisposed children and adults.
RMDL combines different architectures and structures of deep learning and the final output of the model is based on the majority vote. Random Multimodel Deep Learning (RMDL) architecture has been used as another approach to mitigate the effects of the staining problem. To solve this problem, we use color balancing in order to train our model with a varying range of colors. The dataset used in this research is collected from different centers with different staining standards. First, the diagnosis between CD, EE, and Normal biopsies is considered, but the main challenge with this diagnosis technique is the staining problem. In the current study, we propose four diagnosis techniques for these diseases and address their limitations and advantages. 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. Celiac Disease (CD) and Environmental Enteropathy (EE) are common causes of malnutrition and adversely impact normal childhood development.
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