Abstract
During the last two decades, as computer technology has matured and business scenarios have diversified, the scale of application of computer systems in various industries has continued to expand, resulting in a huge increase in industry data. As for the medical industry, huge unstructured data has been accumulated, so exploring how to use medical image data more effectively to efficiently complete diagnosis has an important practical impact. For a long time, China has been striving to promote the process of medical informatization, and the combination of big data and artificial intelligence and other advanced technologies in the medical field has become a hot industry and a new development trend. This paper focuses on cardiovascular diseases and uses relevant deep learning methods to realize automatic analysis and diagnosis of medical images and verify the feasibility of AI-assisted medical treatment. We have tried to achieve a complete diagnosis of cardiovascular medical imaging and localize the vulnerable lesion area. (1) We tested the classical object based on a convolutional neural network and experiment, explored the region segmentation algorithm, and showed its application scenarios in the field of medical imaging. (2) According to the data and task characteristics, we built a network model containing classification nodes and regression nodes. After the multitask joint drill, the effect of diagnosis and detection was also enhanced. In this paper, a weighted loss function mechanism is used to improve the imbalance of data between classes in medical image analysis, and the effect of the model is enhanced. (3) In the actual medical process, many medical images have the label information of high-level categories but lack the label information of low-level lesions. The proposed system exposes the possibility of lesion localization under weakly supervised conditions by taking cardiovascular imaging data to resolve these issues. Experimental results have verified that the proposed deep learning-enabled model has the capacity to resolve the aforementioned issues with minimum possible changes in the underlined infrastructure.
Highlights
With the widespread use of computers and the rapid development of related information industries, people have entered the age of informatization. e scale of application systems in various industries continues to expand, and the industry data generated is exploding, just like a gold mine of data waiting for people to mine and use
We have focused on cardiovascular diseases and use relevant deep learning methods to realize automatic analysis and diagnosis of medical images and verify the feasibility of AI-assisted medical treatment. e scientific contribution of this paper is given as follows: (i) Deep learning-enabled approach is used to realize automatic analysis and diagnosis of medical images and verify the feasibility of AI-assisted medical treatment (ii) Weighted loss function mechanism is used to improve the imbalance of data between classes in medical image analysis, and the effect of the model is enhanced
The computer calculates various possibilities in the real application scenario according to the collected current data and its own model and outputs the decision with the highest probability. e process of using deep learning technology to solve the problem of medical image analysis is the same. e complete system consists of a basic layer, technical layer, and application layer. e base layer is mainly composed of physical devices for model calculation and data storage at the bottom layer. e technology layer uses various types of deep learning algorithms to build models and form effective core technologies. e application layer is the product and application formed by the combination of artificial intelligence technology and specific application scenarios
Summary
With the widespread use of computers and the rapid development of related information industries, people have entered the age of informatization. e scale of application systems in various industries continues to expand, and the industry data generated is exploding, just like a gold mine of data waiting for people to mine and use. A large amount of unstructured image data has been reserved in the medical field, but the reuse rate of stored data still needs to be improved. Erefore, how to use medical imaging data more efficiently to improve diagnosis efficiency and medical level is a major challenge. The country is vigorously advancing the reform of medical information technology, and the application of cutting-edge technologies such as artificial intelligence to the field of medical diagnosis has become a new era choice. With the help of big data and AI technology, can the diagnosis efficiency and accuracy of diagnosis be improved and the diseases of countless patients be cured, and they are helpful to improve the current situation of uneven distribution of medical resources in my country. Relevant medical studies have shown that vulnerable plaques in the cardiovascular system are the main cause of a large number of cardiovascular diseases, and the current diagnosis of cardiovascular diseases mainly depends on doctors reading
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