政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/142698

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政大機構典藏 > 文學院 > 圖書資訊與檔案學研究所 > 期刊論文 > Item 140.119/142698

Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/142698

Title:	Rapid Polyp Classification in Colonoscopy Using Textural and Convolutional Features
Authors:	羅崇銘 Lo, Chung-Ming Yeh, Yu-Hsuan;Tang, Jui-Hsiang;Chang, Chun-Chao;Yeh, Hsing-Jung
Contributors:	圖檔所
Keywords:	colorectal cancer;colon polyp;image features;convolutional neural network
Date:	2022-08
Issue Date:	2022-12-02 15:34:19 (UTC+8)
Abstract:	Colorectal cancer is the leading cause of cancer-associated morbidity and mortality worldwide. One of the causes of developing colorectal cancer is untreated colon adenomatous polyps. Clinically, polyps are detected in colonoscopy and the malignancies are determined according to the biopsy. To provide a quick and objective assessment to gastroenterologists, this study proposed a quantitative polyp classification via various image features in colonoscopy. The collected image database was composed of 1991 images including 1053 hyperplastic polyps and 938 adenomatous polyps and adenocarcinomas. From each image, textural features were extracted and combined in machine learning classifiers and machine-generated features were automatically selected in deep convolutional neural networks (DCNN). The DCNNs included AlexNet, Inception-V3, ResNet-101, and DenseNet-201. AlexNet trained from scratch achieved the best performance of 96.4% accuracy which is better than transfer learning and textural features. Using the prediction models, the malignancy level of polyps can be evaluated during a colonoscopy to provide a rapid treatment plan.
Relation:	Healthcare, Vol.10, No.8, 1494
Data Type:	article
DOI 連結:	https://doi.org/10.3390/healthcare10081494
DOI:	10.3390/healthcare10081494
Appears in Collections:	[圖書資訊與檔案學研究所] 期刊論文

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