Rapid Polyp Classification in Colonoscopy Using Textural and Convolutional Features

Chung Ming Lo, Yu Hsuan Yeh, Jui Hsiang Tang, Chun Chao Chang, Hsing Jung Yeh

研究成果: 雜誌貢獻文章同行評審

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.
原文英語
文章編號1494
期刊Healthcare (Switzerland)
10
發行號8
DOIs
出版狀態已發佈 - 8月 2022

ASJC Scopus subject areas

  • 領導和管理
  • 健康政策
  • 健康資訊學
  • 健康資訊管理

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