# HMIC: Hierarchical Medical Image Classification, A Deep Learning Approach Source: [HMIC: Hierarchical Medical Image Classification, A Deep Learning Approach]() Collection index: [llms.txt]() Data updated: 2025-10-25T22:22:05.587Z Authors: Kamran Kowsari, Rasoul Sali, Lubaina Ehsan, William Adorno, Asad Ali, Sean Moore, Beatrice Amadi, Paul Kelly, Sana Syed, Donald Brown Published: 2020-06-12T15:15:29.000Z ## 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). ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()