Cargando…
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...
Autores principales: | , , |
---|---|
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 |
_version_ | 1783706490461224960 |
---|---|
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. |
format | Online Article Text |
id | pubmed-8195382 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT chiangchenhua automaticclassificationofmedicalimagemodalityandanatomicallocationusingconvolutionalneuralnetwork AT wengchilun automaticclassificationofmedicalimagemodalityandanatomicallocationusingconvolutionalneuralnetwork AT chiuhungwen automaticclassificationofmedicalimagemodalityandanatomicallocationusingconvolutionalneuralnetwork |