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

Author:

Chiang Chen-HuaORCID,Weng Chi-Lun,Chiu Hung-Wen

Abstract

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.

Publisher

Public Library of Science (PLoS)

Subject

Multidisciplinary

Reference16 articles.

1. DICOM demystified: a review of digital file formats and their use in radiological practice;RN Graham;Clin Radiol,2005

2. Managing DICOM images: Tips and tricks for the radiologist;DR Varma;Indian J Radiol Imaging,2012

3. Restoration of Lossy JPEG-Compressed Brain MR Images Using Cross-Domain Neural Networks;KJ Chung;IEEE Signal Processing Letters,2020

4. Quality of DICOM header information for image categorization;M Güld;Proceedings of SPIE—The International Society for Optical Engineering,2002

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