Abstract
Wireless capsule endoscopy is an important method for diagnosing small bowel diseases, but it will collect thousands of endoscopy images that need to be diagnosed. The analysis of these images requires a huge workload and may cause manual reading errors. This article attempts to use neural networks instead of artificial endoscopic image analysis to assist doctors in diagnosing and treating endoscopic images. First, in image preprocessing, the image is converted from RGB color mode to lab color mode, texture features are extracted for network training, and finally, the accuracy of the algorithm is verified. After inputting the retained endoscopic image verification set into the neural network algorithm, the conclusion is that the accuracy of the neural network model constructed in this study is 97.69%, which can effectively distinguish normal, benign lesions, and malignant tumors. Experimental studies have proved that the neural network algorithm can effectively assist the endoscopist's diagnosis and improve the diagnosis efficiency. This research hopes to provide a reference for the application of neural network algorithms in the field of endoscopic images.