Early diagnosis of gastric cancer based on deep learning combined with the spectral-spatial classification method
| Autor: | Weimin Zhang, Xiaoping Zhao, Zhenqiang Chen, Zhao Liu, Siqi Zhu, Liangyu Deng, Xinhao Yang, Xingdan Chen, Yuanpeng Li, Furong Huang |
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| Rok vydání: | 2019 |
| Předmět: |
0303 health sciences
medicine.medical_specialty business.industry Deep learning Cancer Hyperspectral imaging medicine.disease 01 natural sciences Atomic and Molecular Physics and Optics Article Spectral imaging 010309 optics Lesion 03 medical and health sciences Text mining 0103 physical sciences medicine Classification methods Radiology Artificial intelligence Spatial classification medicine.symptom business 030304 developmental biology Biotechnology |
| Zdroj: | Biomedical optics express. 10(10) |
| ISSN: | 2156-7085 |
| Popis: | The development of an objective and rapid method that can be used for the early diagnosis of gastric cancer has important clinical application value. In this study, the fluorescence hyperspectral imaging technique was used to acquire fluorescence spectral images. Deep learning combined with spectral-spatial classification methods based on 120 fresh tissues samples that had a confirmed diagnosis by histopathological examinations was used to automatically identify and extract the “spectral + spatial” features to construct an early diagnosis model of gastric cancer. The model results showed that the overall accuracy for the nonprecancerous lesion, precancerous lesion, and gastric cancer groups was 96.5% with specificities of 96.0%, 97.3%, and 96.7% and sensitivities of 97.0%, 96.3%, and 96.6%, respectively. Therefore, the proposed method can increase the diagnostic accuracy and is expected to be a new method for the early diagnosis of gastric cancer. |
| Databáze: | OpenAIRE |
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