Research on Recognition of Medical Image Detection Based on Neural Network

Bowel cancer, which is easily affected by diet and drugs, has some restrictive factors such as the fecal occult blood test (FOBT) in the routine detection and the high cost and inconvenience of microscopy. In order to break through these restrictive factors, a possible alternative method of FOBT is sought. In this paper, error back propagation neural network (BPNN) algorithm is used, and expression spectrum is used as an auxiliary method to detect medical images, and a colorectal cancer (CRC) diagnosis model based on neural network is constructed. The results show that the accuracy of the model on the training set and the test set are 0.943 and 0.935, respectively, the AUC reaches more than 0.95. Therefore, the CRC diagnosis model based on neural network provides a possible alternative method of FOBT. Experimental results show that the proposed algorithm have high robustness and accuracy, which meets the current clinical needs.