https://scholars.lib.ntu.edu.tw/handle/123456789/635323
標題: | Identification of Early Esophageal Cancer by Semantic Segmentation | 作者: | YU-JEN FANG Mukundan, Arvind Tsao, Yu-Ming Huang, Chien-Wei Wang, Hsiang-Chen |
關鍵字: | ResNet150V2; U-Net; encoder–decoder model; esophageal cancer; narrowband imaging; semantic segmentation; small data; white light imaging | 公開日期: | 八月-2022 | 卷: | 12 | 期: | 8 | 起(迄)頁: | 1204 | 來源出版物: | Journal of personalized medicine | 摘要: | Early detection of esophageal cancer has always been difficult, thereby reducing the overall five-year survival rate of patients. In this study, semantic segmentation was used to predict and label esophageal cancer in its early stages. U-Net was used as the basic artificial neural network along with Resnet to extract feature maps that will classify and predict the location of esophageal cancer. A total of 75 white-light images (WLI) and 90 narrow-band images (NBI) were used. These images were classified into three categories: normal, dysplasia, and squamous cell carcinoma. After labeling, the data were divided into a training set, verification set, and test set. The training set was approved by the encoder-decoder model to train the prediction model. Research results show that the average time of 111 ms is used to predict each image in the test set, and the evaluation method is calculated in pixel units. Sensitivity is measured based on the severity of the cancer. In addition, NBI has higher accuracy of 84.724% when compared with the 82.377% accuracy rate of WLI, thereby making it a suitable method to detect esophageal cancer using the algorithm developed in this study. |
URI: | https://www.scopus.com/record/display.uri?eid=2-s2.0-85137392685&doi=10.3390%2fjpm12081204&origin=inward&txGid=c26b4936a02066302787f7fe2ea75919 https://scholars.lib.ntu.edu.tw/handle/123456789/635323 |
ISSN: | 2075-4426 | DOI: | 10.3390/jpm12081204 |
顯示於: | 醫學系 |
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