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HMIC: Hierarchical Medical Image Classification, A Deep Learning Approach
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...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8346231/ https://www.ncbi.nlm.nih.gov/pubmed/34367687 http://dx.doi.org/10.3390/info11060318 |
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author | Kowsari, Kamran Sali, Rasoul Ehsan, Lubaina Adorno, William Ali, Asad Moore, Sean Amadi, Beatrice Kelly, Paul Syed, Sana Brown, Donald |
author_facet | Kowsari, Kamran Sali, Rasoul Ehsan, Lubaina Adorno, William Ali, Asad Moore, Sean Amadi, Beatrice Kelly, Paul Syed, Sana Brown, Donald |
author_sort | Kowsari, Kamran |
collection | PubMed |
description | 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). |
format | Online Article Text |
id | pubmed-8346231 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
record_format | MEDLINE/PubMed |
spelling | pubmed-83462312021-08-06 HMIC: Hierarchical Medical Image Classification, A Deep Learning Approach Kowsari, Kamran Sali, Rasoul Ehsan, Lubaina Adorno, William Ali, Asad Moore, Sean Amadi, Beatrice Kelly, Paul Syed, Sana Brown, Donald Information (Basel) Article 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). 2020-06-12 2020-06 /pmc/articles/PMC8346231/ /pubmed/34367687 http://dx.doi.org/10.3390/info11060318 Text en https://creativecommons.org/licenses/by/4.0/This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) ). |
spellingShingle | Article Kowsari, Kamran Sali, Rasoul Ehsan, Lubaina Adorno, William Ali, Asad Moore, Sean Amadi, Beatrice Kelly, Paul Syed, Sana Brown, Donald HMIC: Hierarchical Medical Image Classification, A Deep Learning Approach |
title | HMIC: Hierarchical Medical Image Classification, A Deep Learning Approach |
title_full | HMIC: Hierarchical Medical Image Classification, A Deep Learning Approach |
title_fullStr | HMIC: Hierarchical Medical Image Classification, A Deep Learning Approach |
title_full_unstemmed | HMIC: Hierarchical Medical Image Classification, A Deep Learning Approach |
title_short | HMIC: Hierarchical Medical Image Classification, A Deep Learning Approach |
title_sort | hmic: hierarchical medical image classification, a deep learning approach |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8346231/ https://www.ncbi.nlm.nih.gov/pubmed/34367687 http://dx.doi.org/10.3390/info11060318 |
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