Medical Image Analysis and Recognition Based on Deep Convolution Neural Network

DCNN has great application potential in the field of medical image analysis because of its powerful ability of automatic feature extraction and efficient data processing. This article aims to explore the application potential of DCNN (Deep Convolution Neural Network) in the field of medical image analysis and recognition, so as to solve the limitations faced by traditional methods in processing complex medical images. In order to achieve this goal, a DCNN model is constructed specifically for the characteristics of medical images. During the experiment, a large-scale data set containing various types of medical images is selected and strictly divided into training set, verification set and test set to ensure the reliability and generalization performance of the experimental results. Through the comprehensive consideration of several evaluation indexes, DCNN model shows excellent performance on the test set, which not only reaches a high level of accuracy, but also excels other methods in key indexes such as recall rate, F1 score and AUC-ROC curve. The results show that DCNN has great potential and application value in the field of medical image analysis and recognition, and can provide more accurate and efficient auxiliary tools for clinical diagnosis and treatment.