Micro-hyperspectral breast cancer tissue image analysis based on neural network

Objective To explore the feasibility and value of neural network combined with micro-hyperspectral imaging in identifying breast cancer tissue. Methods The micro-hyperspectral imaging technology was used to collect image data of breast cancer tissue, and the micro-hyperspectral breast tissue image analysis method based on neural network was used to realize the automatic classification and regional division of breast cancer tissue. Meanwhile, data preprocessing method was proposed to improve the signal to noise ratio of the image, and map information was trained by neural network to identify breast tissue lesions and highlight them for visualization. Results The micro-hyperspectral breast tissue image analysis method based on neural network utilized two characteristics of the images at the same time, and it was better than traditional color pathological images in identifying breast tissue. Conclusion The micro-hyperspectral breast tissue image analysis method based on neural network can provide more characteristic sample information compared with traditional color pathology images, and may serve as an effective complement to traditional color pathological images. With the support of neural network, the micro-hyperspectral imaging technology has prospects in analyzing breast cancer tissue.