Abstract
In order to explore the application of hyperspectral technology in the pathological diagnosis of tumor tissue, we used microscopic hyperspectral imaging technology to establish a hyperspectral database of 30 patients with gastric cancer. Based on the difference in spectral-spatial features between gastric cancer tissue and normal tissue in the wavelength of 410-910 nm, we propose a deep-learning model-based analysis method for gastric cancer tissue. The microscopic hyperspectral feature and individual difference of gastric tissue, spatial-spectral joint feature and medical contact are studied. The experimental results show that the classification accuracy of proposed model for cancerous and normal gastric tissue is 97.57%, the sensitivity and specificity of gastric cancer tissue are 97.19% and 97.96% respectively. Compared with the shallow learning method, CNN can fully extract the deep spectral-spatial features of tumor tissue. The combination of deep learning model and micro-spectral analysis provides new ideas for the research of medical pathology.
Highlights
At present, precision medicine research has become the focus of biomedical field
Focusing on the above three issues for modeling and analysis, we establish a Spectral-Convolutional Neural Network (CNN) classification model (Spec-CNN for short) based on the spectral features of gastric cancer tissue to investigate the effect of different structural parameters on model performance
The main contribution of this study is to explore the spatial-spectral features of tumor tissue by the microscopic hyperspectral technique and deep learning method, and to establish a basis for further research on the medical contacts between spectral features of tumor tissue with diagnostic criteria, mutation, prognosis, and so on
Summary
Precision medicine research has become the focus of biomedical field. The new diagnosis and treatment of malignant tumor is one of the important research contents [1]. Classification based on the location and cytological characteristic of primary tumor can no longer meet the practical needs. Because in traditional histopathology [2], pathologists usually need to perform a series of work such as fixation and staining on pathological sections. The whole diagnosis process is cumbersome and time consuming. The workload is large and the diagnosis result is affected by human experience. With the rapid development of new medical imaging technology and medical analysis, future histopathology is expected to achieve greater progress and innovation on the basis of computer-aided analysis
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