HMIC: Hierarchical Medical Image Classification, A Deep Learning Approach

TL;DR

This paper introduces HMIC, a hierarchical deep learning approach for medical image classification that improves diagnostic accuracy by considering the clinical image hierarchy, demonstrated on small bowel biopsy images.

Contribution

The paper presents a novel hierarchical classification framework using deep learning models, differing from traditional flat multi-class methods in medical imaging.

Findings

Hierarchical approach improves classification accuracy.

Effective differentiation of disease severity levels.

Demonstrated on small bowel biopsy images.

Abstract

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).