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ResNet and its application to medical image processing: Research progress and challenges
Abstract
Background and objective Deep learning, a novel approach and subset of machine learning, has drawn a growing amount of attention from computer vision researchers in recent years. This method has drawn a lot of interest because of its extraordinary ability to interpret medical pictures, especially when combined with residual neural networks, which have helped to progress the field.Methods In this paper, the following research is carried out on the residual network. First, the research status of ResNet in the medical field is introduced. The fundamental idea behind the residual neural network is then explained, along with the residual unit, its many structures, and the network architecture. Second, four aspects of the widespread use of residual neural networks in medical image processing are discussed: lung tumor, diagnosis of skin diseases, diagnosis of breast diseases, and diagnosis of diseases of the brain. Finally, the main issues and ResNet's future development in the area of processing medical images are discussed.Results In the area of medical graph processing, residual neural networks have made strides and have had success in the clinical auxiliary diagnosis of serious illnesses such as lung tumors, breast cancer, skin conditions, and cardiovascular and cerebrovascular diseases.Conclusion We thoroughly sorted out the most recent developments in residual neural network research and their use in medical image processing, which serves as a crucial point of reference for this field of study. It offers a helpful reference for further promoting the application and research of the ResNet model in the field of medical image processing by summarising the application status and issues of the ResNet model in the field of medical image processing and putting forwards some future development directions.
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“ResNet: Introduced residual connections to enable training of very deep networks without vanishing gradients [60].”
From Artificial Intelligence in Dermatopathology: An Update and Review of the Current Literature (2026) · cites this paper as [60]
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Metadata
- Authors
- Xu, W., Fu, Y.-L., & Zhu, D.
- Affiliations
- Fuzhou University (CN)
- Jiangxi University of Water Resources and Electric Power (CN)
- Nanjing Agricultural University (CN)
- Corresponding author
- Fu, You-Lei
- Venue
- Computer Methods and Programs in Biomedicine
- Publisher
- Elsevier BV
- Published
- 2023-10
- Volume
- 240
- Pages
- 107660
- ISSN
- 0169-2607 (ISSN-L)
- OpenAlex
- W4379880150
- Cited by
- 416
- References
- 44
- Citations by year
- 2026: 144 · 2025: 183 · 2024: 80 · 2023: 7
- FWCI
- 39.03
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Computer Methods and Programs in Biomedicine (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- ResNet, medical image processing, residual blocks, lung cancer detection, dermatological diagnosis, breast cancer diagnosis, brain disease diagnosis, deep learning in healthcare
- MeSH terms
- Machine Learning, Breast Neoplasms, Diagnosis, Computer-Assisted, Female, Humans, Image Processing, Computer-Assisted, Lung Neoplasms, Neural Networks, Computer
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