Research on Optimization of Automatic Medical Image Recognition System Based on Deep Learning

Abstract

With the rapid development of deep learning technology, its importance to medical image recognition has been highlighted, and medical image recognition has become the core of medical development. The automatic and autonomous learning based on deep learning has greatly improved the accuracy and speed of medical image recognition, especially in the early identification of diseases and the determination of the location of diseases. However, there are still some problems such as too little data, too complicated model design and high computational cost. This paper mainly analyzes the optimization of the automatic recognition system of medical images based on deep learning, and proposes some possible methods, such as data enhancement, model structure optimization, loss function setting and transfer learning, to improve the stability and accuracy of the system.