| dc.contributor | 圖檔所 | |
| dc.creator (作者) | 羅崇銘 | |
| dc.creator (作者) | Lo, Chung-Ming | |
| dc.creator (作者) | Yeh, Yu-Hsuan;Tang, Jui-Hsiang;Chang, Chun-Chao;Yeh, Hsing-Jung | |
| dc.date (日期) | 2022-08 | |
| dc.date.accessioned | 2-Dec-2022 15:34:19 (UTC+8) | - |
| dc.date.available | 2-Dec-2022 15:34:19 (UTC+8) | - |
| dc.date.issued (上傳時間) | 2-Dec-2022 15:34:19 (UTC+8) | - |
| dc.identifier.uri (URI) | http://nccur.lib.nccu.edu.tw/handle/140.119/142698 | - |
| dc.description.abstract (摘要) | 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. | |
| dc.format.extent | 106 bytes | - |
| dc.format.mimetype | text/html | - |
| dc.relation (關聯) | Healthcare, Vol.10, No.8, 1494 | |
| dc.subject (關鍵詞) | colorectal cancer; colon polyp; image features; convolutional neural network | |
| dc.title (題名) | Rapid Polyp Classification in Colonoscopy Using Textural and Convolutional Features | |
| dc.type (資料類型) | article | |
| dc.identifier.doi (DOI) | 10.3390/healthcare10081494 | |
| dc.doi.uri (DOI) | https://doi.org/10.3390/healthcare10081494 | |
