HMIC: Hierarchical Medical Image Classification, A Deep Learning Approach
This work addresses a domain-specific problem in medical imaging for improved diagnosis, but it appears incremental as it adapts deep learning to a hierarchical structure.
The paper tackles the problem of medical image classification by proposing a hierarchical approach (HMIC) instead of traditional multi-class classification, achieving classification of small bowel biopsy images into three parent categories and four child severity classes.
Image classification is central to the big data revolution in medicine. Improved information processing methods for diagnosis and classification of digital medical images have shown to be successful via deep learning approaches. As this field is explored, there are limitations to the performance of traditional supervised classifiers. This paper outlines an approach that is different from the current medical image classification tasks that view the issue as multi-class classification. We performed a hierarchical classification using our Hierarchical Medical Image classification (HMIC) approach. HMIC uses stacks of deep learning models to give particular comprehension at each level of the clinical picture hierarchy. 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). For the child level, Celiac Disease Severity is classified into 4 classes (I, IIIa, IIIb, and IIIC).