Research And Implementation Of Medical Image Deep Learning Model Deployment Technology Based On Observer Pattern

The wide application of deep learning in medicine has made remarkable achievements,especially in the fields of auxiliary diagnosis,mapping of lesions and medical image analysis.The basic browser of medical image is DICOM film reading software.However,most DICOM film reading software only has traditional film reading function,and cannot use advanced deep learning model to assist film reading.Therefore,it is very important to study an effective deep learning model deployment tool.This paper discusses the design and implementation method of deep learning model deployment tool,aiming at providing an efficient,practical,simple and easy to use deep learning module model deployment scheme for DICOM film reading software developers.This paper covers the complete process of deep learning model from training to final application,providing more academic thoughts and references for researchers and developers.The design of this tool mainly involves two aspects:deep learning model deployment framework and deep learning model application.In terms of the design of deep learning model deployment framework,we compare the common deep learning model deployment framework on C++ platform,and choose OpenCV:DNN as the deep learning model deployment framework of this tool,and summarize it as the deep learning model management module.In the application of deep learning model,the application is divided into observer mode module,data management module,task pipeline module and image reading acceleration module,so as to improve the efficiency and practicability of deep learning model application.The observer module realizes efficient communication between pipeline nodes by using the relevance of the observer mode;the deep learning model management module realizes the multi-direction transmission of messages;the task pipeline module integrates the characteristics of multiple core modules;the image reading acceleration module and other modules also make significant contributions to the optimization of the tool.The main contribution of this tool is to solve the problem of the lack of a complete,reasonable and efficient deep learning model deployment process tool in the commercial market.It takes many factors into account innovatively and effectively solves the problems encountered in the deployment of deep learning model in practical application.At the same time,it provides a method to integrate deep learning model into DICOM film reading software for DICOM software developers.The design and implementation of this tool provide a reliable deep learning model deployment framework and an efficient deep learning model application scheme for DICOM reader software developers.By using this tool,developers can integrate advanced deep learning models into DICOM film reading software,so as to improve the analysis and diagnosis efficiency of medical images and provide better support for medical diagnosis.In the future,the capabilities of the tool will be further refined to meet a wider range of deep learning model deployment requirements.It will also continue to explore the potential of deep learning in medical applications to provide better solutions for medical image analysis and diagnosis.