This paper combines Struts and Hibernate two architectures together, using DAO (Data Access Object) to store and access data. Then a set of dual-mode humidity medical image library suitable for deep network is established, and a dual-mode medical image assisted diagnosis method based on the image is proposed. Through the test of various feature extraction methods, the optimal operating characteristic under curve product (AUROC) is 0.9985, the recall rate is 0.9814, and the accuracy is 0.9833. This method can be applied to clinical diagnosis, and it is a practical method. Any outpatient doctor can register quickly through the system, or log in to the platform to upload the image to obtain more accurate images. Through the system, each outpatient physician can quickly register or log in to the platform for image uploading, thus obtaining more accurate images. The segmentation of images can guide doctors in clinical departments. Then the image is analyzed to determine the location and nature of the tumor, so as to make targeted treatment.

结合Struts和Hibernate两种架构，使用DAO（数据访问对象）来存储和访问数据。建立了一套适用于深度网络的双模湿度医学图像库，并提出了一种基于图像的双模医学图像辅助诊断方法。该方法在各种特征提取方法的测试中，最佳工作特性下曲线积分（AUROC）为0.9985，召回率为0.9814，准确率为0.9833。这种方法可以应用于临床诊断，是一种实用的方法。通过系统，每个门诊医生可以快速注册或登录平台进行图像上传，从而获得更准确的图像。图像的分割可以指导临床科室中的医生，然后分析图像以确定肿瘤的位置和性质，从而进行有针对性的治疗。

基于计算机深度学习的医学图像智能辅助诊断系统研究