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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...

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Autores principales: Kowsari, Kamran, Sali, Rasoul, Ehsan, Lubaina, Adorno, William, Ali, Asad, Moore, Sean, Amadi, Beatrice, Kelly, Paul, Syed, Sana, Brown, Donald
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2020
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).
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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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