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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Autores principales:	Chen-Hua Chiang, Chi-Lun Weng, Hung-Wen Chiu
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/e99ea678c0fe4594864b353b4cbfed90
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spelling	oai:doaj.org-article:e99ea678c0fe4594864b353b4cbfed902021-12-02T20:10:43ZAutomatic classification of medical image modality and anatomical location using convolutional neural network.1932-620310.1371/journal.pone.0253205https://doaj.org/article/e99ea678c0fe4594864b353b4cbfed902021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0253205https://doaj.org/toc/1932-6203Modern 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.Chen-Hua ChiangChi-Lun WengHung-Wen ChiuPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 6, p e0253205 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Chen-Hua Chiang Chi-Lun Weng Hung-Wen Chiu Automatic classification of medical image modality and anatomical location using convolutional neural network.
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.
format	article
author	Chen-Hua Chiang Chi-Lun Weng Hung-Wen Chiu
author_facet	Chen-Hua Chiang Chi-Lun Weng Hung-Wen Chiu
author_sort	Chen-Hua Chiang
title	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_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_sort	automatic classification of medical image modality and anatomical location using convolutional neural network.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/e99ea678c0fe4594864b353b4cbfed90
work_keys_str_mv	AT chenhuachiang automaticclassificationofmedicalimagemodalityandanatomicallocationusingconvolutionalneuralnetwork AT chilunweng automaticclassificationofmedicalimagemodalityandanatomicallocationusingconvolutionalneuralnetwork AT hungwenchiu automaticclassificationofmedicalimagemodalityandanatomicallocationusingconvolutionalneuralnetwork
_version_	1718374978391375872

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

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