Research On Medical Image Classification Method Based On Automatic Design Neural Network

The application of computer technology in the medical field has been one of the hot research fields in recent decades,and the rapid development of deep learning in recent years has made the research of medical image processing based on deep learning also received many scholars' attention at home and abroad in recent years.It is widely concerned that in many areas such as medical image recognition,detection,segmentation,and tracking,it has reached a level that is difficult to achieve by other non-deep learning methods,and has gradually become the most popular research direction in the medical image field.However,many current studies show that the performance of image classification based on deep learning depends on the structure of the neural network.Therefore,if medical image classification technology based on deep learning is to be further developed,it will achieve a higher level of performance than existing deep learning methods.Better classification performance should be studied in combination with the characteristics of the medical image itself and the characteristics of the neural network structure.However,as the structure of neural networks becomes more and more complex,it becomes more and more difficult to improve the structure of neural networks through artificial design.In order to further improve the performance of medical image classification and make the computer recognize the disease more accurately,this thesis studies the automatic design of neural network technology and combines the characteristics of medical images to design a set of methods suitable for medical image classification.The search on the medical image data set aims to find an optimal neural network structure suitable for this data set.This process is automatically completed by the computer without manual intervention.Compared with ordinary artificial design neural network structures,it is in this category.The classification task will perform better.The main research contents of this thesis include two aspects: one is the structure search method of neural network;the other is how to adapt the automatic design neural network technology to medical image classification to achieve better classification performance than traditional deep learning methods.First,we studied the method of automatically designing neural network.We studied the design method of neural network structure.By understanding the characteristics of the existing neural network structures,we designed a reasonable neural network structure composition method.Using the algorithmic ideas of reinforcement learning,a neural network structure search algorithm with both efficiency and accuracy is designed to screen neural network structures with excellent classification performance.At the same time,with the help of ideas and algorithms such as generating adversarial networks,parameter sharing,and layer-by-layer stacking of network structures,the search efficiency is optimized and the calculation time is greatly reduced.Secondly,we use the previous research results of automatic neural network design and combine the characteristics of medical images to design a complete medical image classification method based on automatic design neural network technology.It is experimentally verified that this method can achieve better results than traditional methods Classification effect.In addition,we aim at some typical problems in medical image classification tasks,such as large amounts of data,ambiguous labels,and extreme imbalance in the distribution of data categories.We use a variety of methods for experiments and analysis to obtain better targeted measures.