ResNet and its application to medical image processing: Research progress and challenges.
Disease diagnosis
Medical image
Residual connection
Residual neural network (ResNet)
Residual units
Journal
Computer methods and programs in biomedicine
ISSN: 1872-7565
Titre abrégé: Comput Methods Programs Biomed
Pays: Ireland
ID NLM: 8506513
Informations de publication
Date de publication:
Oct 2023
Oct 2023
Historique:
received:
29
05
2022
revised:
04
06
2023
accepted:
07
06
2023
medline:
29
8
2023
pubmed:
16
6
2023
entrez:
15
6
2023
Statut:
ppublish
Résumé
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. 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. 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. 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.
Sections du résumé
BACKGROUND AND OBJECTIVE
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
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
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
CONCLUSIONS
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.
Identifiants
pubmed: 37320940
pii: S0169-2607(23)00325-5
doi: 10.1016/j.cmpb.2023.107660
pii:
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
107660Informations de copyright
Copyright © 2023 Elsevier B.V. All rights reserved.
Déclaration de conflit d'intérêts
Declaration of Competing Interest The authors declare that there is no conflict of interests in this article.