Medical image diagnosis and auxiliary analysis based on deep learning
With the development of artificial intelligence technology and Internet medicine, the use of deep learning to realize the location, segmentation and classification of lesions in medical images has become an inevitable trend in the development of new medical models. The application of artificial inte...
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| Main Author: | |
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| Format: | Article |
| Language: | English |
| Published: |
EDP Sciences
2025-01-01
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| Series: | BIO Web of Conferences |
| Online Access: | https://www.bio-conferences.org/articles/bioconf/pdf/2025/17/bioconf_icbemd2025_02006.pdf |
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| Summary: | With the development of artificial intelligence technology and Internet medicine, the use of deep learning to realize the location, segmentation and classification of lesions in medical images has become an inevitable trend in the development of new medical models. The application of artificial intelligence technology in medical image diagnosis to improve the efficiency and accuracy of medical image diagnosis has become a hot topic in recent years. Deep learning technology has achieved great success in the field of image processing, and its application in medical image-assisted diagnosis has become more common. This paper introduces the concept and development of deep learning, as well as the development process of deep learning models, convolutional neural networks, and deep belief network models, and reviews the current status of their application research in medical image analysis. First, the current status of deep learning technology in medical image-assisted diagnosis is analyzed, and then the specific application of deep learning in medical images is introduced from the three application areas of classification detection and segmentation. By reading the literature, the application of deep learning models in the field of medical image analysis is sorted out, and on this basis, some key application areas are highlighted, and then the problems existing in the basic deep learning model are discussed, and the problems currently encountered by the deep learning model and the prospects and prospects of deep learning in the medical field are understood. |
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| ISSN: | 2117-4458 |