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Classification of Computed Tomography Images in Different Slice Positions Using Deep Learning

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Please use this identifier to cite or link to this item:http://hdl.handle.net/2115/71438

Title: Classification of Computed Tomography Images in Different Slice Positions Using Deep Learning
Authors: Sugimori, Hiroyuki Browse this author →KAKEN DB
Issue Date: 16-Jul-2018
Publisher: Hindawi Publishing Corporation
Journal Title: Journal of healthcare engineering
Volume: 2018
Start Page: 1753480
Publisher DOI: 10.1155/2018/1753480
PMID: 30123439
Abstract: This study aimed at elucidating the relationship between the number of computed tomography (CT) images, including data concerning ihc accuracy of models and contrast enhancement for classifying the images. We enrolled 1539 patients who underwent contrast or noncontrast CT imaging, followed by dividing the CT imaging dataset for creating classification models into 10 classes for brain, neck, chest, abdomen, and pelvis with contrast-enhanced and plain imaging. The number of images prepared in each class were 100, 500, 1000, 2000, 3000, 4000, 5000, 6000, 7000, 8000, 9000, and 10,000. Accordingly, the names of datasets were defined as 0.1K,0.5K,1K,2K,3K,4K,5K,6K,7K,8K,9K, and 10K, respectively. We subsequently created and evaluated the models and compared the convolutional neural network (CNN) architecture between AlexNet and GoogLeNet. The time required for training models of AlexNet was lesser than that for GoogLeNet. The best overall accuracy for the classification of 10 classes was 0.721 with the 10K dataset of GoogLeNet. Furthermore, the best overall accuracy for the classification of the slice position without contrast media was 0.862 with the 2K dataset of AlexNet.
Rights: http://creativecommons.org/licenses/by/4.0/
Type: article
URI: http://hdl.handle.net/2115/71438
Appears in Collections:保健科学院・保健科学研究院 (Graduate School of Health Sciences / Faculty of Health Sciences) > 雑誌発表論文等 (Peer-reviewed Journal Articles, etc)

Submitter: 杉森 博行

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