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Automatic classification of medical image modality and anatomical location using convolutional neural network

Modern radiologic images comply with DICOM (digital imaging and communications in medicine) standard, which, upon conversion to other image format, would lose its image detail and information such as patient demographics or type of image modality that DICOM format carries. As there is a growing inte...

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Detalles Bibliográficos
Autores principales: Chiang, Chen-Hua, Weng, Chi-Lun, Chiu, Hung-Wen
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8195382/
https://www.ncbi.nlm.nih.gov/pubmed/34115822
http://dx.doi.org/10.1371/journal.pone.0253205
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author Chiang, Chen-Hua
Weng, Chi-Lun
Chiu, Hung-Wen
author_facet Chiang, Chen-Hua
Weng, Chi-Lun
Chiu, Hung-Wen
author_sort Chiang, Chen-Hua
collection PubMed
description Modern radiologic images comply with DICOM (digital imaging and communications in medicine) standard, which, upon conversion to other image format, would lose its image detail and information such as patient demographics or type of image modality that DICOM format carries. As there is a growing interest in using large amount of image data for research purpose and acquisition of large amount of medical image is now a standard practice in the clinical setting, efficient handling and storage of large amount of image data is important in both the clinical and research setting. In this study, four classes of images were created, namely, CT (computed tomography) of abdomen, CT of brain, MRI (magnetic resonance imaging) of brain and MRI of spine. After converting these images into JPEG (Joint Photographic Experts Group) format, our proposed CNN architecture could automatically classify these 4 groups of medical images by both their image modality and anatomic location. We achieved excellent overall classification accuracy in both validation and test sets (> 99.5%), specificity and F1 score (> 99%) in each category of this dataset which contained both diseased and normal images. Our study has shown that using CNN for medical image classification is a promising methodology and could work on non-DICOM images, which could potentially save image processing time and storage space.
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spelling pubmed-81953822021-06-21 Automatic classification of medical image modality and anatomical location using convolutional neural network Chiang, Chen-Hua Weng, Chi-Lun Chiu, Hung-Wen PLoS One Research Article Modern radiologic images comply with DICOM (digital imaging and communications in medicine) standard, which, upon conversion to other image format, would lose its image detail and information such as patient demographics or type of image modality that DICOM format carries. As there is a growing interest in using large amount of image data for research purpose and acquisition of large amount of medical image is now a standard practice in the clinical setting, efficient handling and storage of large amount of image data is important in both the clinical and research setting. In this study, four classes of images were created, namely, CT (computed tomography) of abdomen, CT of brain, MRI (magnetic resonance imaging) of brain and MRI of spine. After converting these images into JPEG (Joint Photographic Experts Group) format, our proposed CNN architecture could automatically classify these 4 groups of medical images by both their image modality and anatomic location. We achieved excellent overall classification accuracy in both validation and test sets (> 99.5%), specificity and F1 score (> 99%) in each category of this dataset which contained both diseased and normal images. Our study has shown that using CNN for medical image classification is a promising methodology and could work on non-DICOM images, which could potentially save image processing time and storage space. Public Library of Science 2021-06-11 /pmc/articles/PMC8195382/ /pubmed/34115822 http://dx.doi.org/10.1371/journal.pone.0253205 Text en © 2021 Chiang et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Chiang, Chen-Hua
Weng, Chi-Lun
Chiu, Hung-Wen
Automatic classification of medical image modality and anatomical location using convolutional neural network
title Automatic classification of medical image modality and anatomical location using convolutional neural network
title_full Automatic classification of medical image modality and anatomical location using convolutional neural network
title_fullStr Automatic classification of medical image modality and anatomical location using convolutional neural network
title_full_unstemmed Automatic classification of medical image modality and anatomical location using convolutional neural network
title_short Automatic classification of medical image modality and anatomical location using convolutional neural network
title_sort automatic classification of medical image modality and anatomical location using convolutional neural network
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8195382/
https://www.ncbi.nlm.nih.gov/pubmed/34115822
http://dx.doi.org/10.1371/journal.pone.0253205
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