Journal
JOURNAL OF PERSONALIZED MEDICINE
Volume 12, Issue 8, Pages -Publisher
MDPI
DOI: 10.3390/jpm12081204
Keywords
esophageal cancer; small data; semantic segmentation; encoder-decoder model; U-Net; ResNet150V2; white light imaging; narrowband imaging
Funding
- Ministry of Science and Technology, The Republic of China [MOST 111-2221-E-194-007]
- Advanced Institute of Manufacturing with High-tech Innovations (AIM-HI)
- Center for Innovative Research on Aging Society (CIRAS) from The Featured Areas Research Center Program by the Ministry of Education (MOE)
- National Chung Cheng University-National Taiwan University Hospital Yunlin Branch Joint Research Program [CCU-NTUHYB-2022-01]
- Kaohsiung Armed Forces General Hospital in Taiwan [111-010]
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In this study, semantic segmentation and neural networks were used to predict and label early-stage esophageal cancer. Results showed that the accuracy rate of the narrow-band image method was higher compared to white-light images, indicating its potential for esophageal cancer detection.
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.
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