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Open AccessArticle

HMIC: Hierarchical Medical Image Classification, A Deep Learning Approach

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Department of Systems and Information Engineering, University of Virginia, Charlottesville, VA 22904, USA
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Office of Health Informatics and Analytics, University of California, Los Angeles (UCLA), CA 90095, USA
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Sensing Systems for Health Lab, University of Virginia, Charlottesville, VA 22911, USA
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Department of Pediatrics, School of Medicine, University of Virginia, Charlottesville, VA 22903, USA
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Department of Paediatrics and Child Health, The Aga Khan University, Karachi 74800, Pakistan
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Tropical Gastroenterology and Nutrition Group, University of Zambia School of Medicine, 32379 Lusak, Zambia
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Blizard Institute, Barts and The London School of Medicine, Queen Mary University of London, London E1 4NS, UK
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School of Data Science, University of Virginia, Charlottesville, VA 22904, USA
*
Authors to whom correspondence should be addressed.
Information 2020, 11(6), 318; https://doi.org/10.3390/info11060318
Received: 6 May 2020 / Revised: 9 June 2020 / Accepted: 10 June 2020 / Published: 12 June 2020
(This article belongs to the Section Information Processes)
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). View Full-Text
Keywords: deep Learning; hierarchical classification; hierarchical medical image classification; medical imaging deep Learning; hierarchical classification; hierarchical medical image classification; medical imaging
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Kowsari, K.; Sali , R.; Ehsan, L.; Adorno , W.; Ali, A.; Moore, S.; Amadi, B.; Kelly, P.; Syed, S.; Brown, D. HMIC: Hierarchical Medical Image Classification, A Deep Learning Approach. Information 2020, 11, 318.

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