Algorithm improvement of neural network in endoscopic image recognition of upper digestive tract system. Issue 4 (1st December 2021)

• Record Type: Journal Article
• Authors: Lu, Bin
• Is Part Of: Expert systems. Volume 40:Issue 4(2023)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-12-01
• Subjects: convolutional neural network -- endoscopic image -- image analysis -- image preprocessing -- upper digestive tract; Expert systems (Computer science); 006.33
• DOI: 10.1111/exsy.12912
• Languages: English
• ISSNs: 0266-4720

Abstract

Abstract: The development of effective gastrointestinal diseases computer‐aided diagnosis tools and automatic image quality assessment algorithms is very important to improve the effectiveness of diagnosis and treatment. In order to further study the application of neural network algorithm in the endoscopic image of upper digestive tract, improve the efficiency of neural network algorithm in the field of endoscopic image. In this study, neural network algorithms were used to identify endoscopic images of the upper digestive tract. 1335 cases with upper gastrointestinal endoscopic images were collected. After the data was enlarged, it was randomly divided into training set and test set according to the proportion, and the obtained training set was input. After convolutional neural network training, an algorithm model was established in the institute. 1653 test set data samples were input into the neural network to verify the accuracy. Finally, the accuracy of the neural root network model constructed in this study reached 0.0942. Through horizontal comparison, it can be concluded that the neural network model proposed in this study not only has a higher accuracy rate, but also is better than the current existing related neural network algorithms. Based on the above experimental verification, it can be concluded that the upper gastrointestinal endoscopic image recognition algorithm based on neural network proposed in this study can more accurately and effectively identify the lesions in the upper gastrointestinal endoscopic images.