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ResNet and its application to medical image processing: Research progress and challenges

Xu, Wanni, Fu, You-Lei, Zhu, Dongmei·2023·Computer Methods and Programs in Biomedicine

DOI 10.1016/j.cmpb.2023.107660

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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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Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
Proceedings of IEEE Conference on Computer Vision and Pattern Recognition
(1993) · doi:10.1109/cvpr.1993 · Cited by 17,989
A survey on deep learning in medical image analysis
Litjens, G., Kooi, T., Bejnordi, B. E., Setio, A. A. A., Ciompi, F., Ghafoorian, M., van der Laak, J. A. W. M., van Ginneken, B., & Sánchez, C. I. (2017) · Medical Image Analysis · doi:10.1016/j.media.2017.07.005 · Cited by 15,332
Identity mappings in deep residual networks
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46493-0_38 · Cited by 10,741
3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation
Çiçek, Ö., Abdulkadir, A., Lienkamp, S. S., Brox, T., & Ronneberger, O. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46723-8_49 · Cited by 7,945
The multimodal brain tumor image segmentation benchmark (BRATS)
Menze, B. H., Jakab, A., Bauer, S., Kalpathy-Cramer, J., Farahani, K., Kirby, J., Burren, Y., Porz, N., Slotboom, J., Wiest, R., et al. (2015) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2014.2377694 · Cited by 6,781
Residual Attention Network for Image Classification
Wang, F., Jiang, M., Qian, C., Yang, S., Li, C., Zhang, H., Wang, X., & Tang, X. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.683 · Cited by 4,073
SphereFace: Deep Hypersphere Embedding for Face Recognition
Liu, W., Wen, Y., Yu, Z., Li, M., Raj, B., & Song, L. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.713 · Cited by 2,280
VoxResNet: Deep voxelwise residual networks for brain segmentation from 3D MR images
Chen, H., Dou, Q., Yu, L., Qin, J., & Heng, P. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.04.041 · Cited by 781
Deep Pyramidal Residual Networks
Han, D., Kim, J., & Kim, J. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.668 · Cited by 715

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Research works printed but not linked: 16

Kang, 2015, Unsupervised deep learning method for color image recognition, J. Comput. Appl., 35, 2636−2639
Chu, 2018, Breast cancer diagnosis sys- tem based on transfer leaming and deep convolutional neural net- works, Laser Optoelectron. Progress, 55
Clevert D.A., Unterthiner T. and Hochreiter S. 2016. Fast and accurate deep network learning by exponential linear units (ELUs) [EB/0L]. [202040526]. https://arxiv.org/pdf/1511.07289.pdf. · arXiv
Zhao, 2020, A review of image classification algorithms based on deep residual network [J], Comput. Syst. Appl., 29, 14
Tao, 2020, Research on residual neural network and its application in medical image processing [J], J. Electron., 48, 1436
Veit A., Wilber M., Belongie S. Residual networks behave like en-sembles of relatively shallow networks [EB/OL]. (2016-10-27). https://arxiv.org/abs/1605.06431. · arXiv
Jun, 2021, A multiscale residual pyramid attention network for medical image fusion[J], Biomed. Signal Process. Control, 66
Zheng, 2018, Survey on medical imagecomputer aided detection and diagnosis systems, J. Soft-ware, 29, 1471
Gong, 2015, Benign or malignant classifi-cation of lung nodules based on semantic attributes, Acta Electronica Sinica, 43, 2476
Liang, 2018, Lung tumor images recognition based on PSO-ConvK convolutional neural net-work, J. Shandong University (Engineering Science), 48, 77
Dai, 2018, Benign or malignant lung nodulesclassification model based on modified DenseNet, Chin. J. Med. Imag. Technol., 34, 1104
Yao, 2019, Pulmonary nodule detection via hybrid loss based joint fine-tuning and multi-scale classification, Appl. Res. Comput., 36, 2872
Zhu, 2019, U-Net pulmonary nodule detection algorithm based on multi-scale feature structure, Comput. Eng., 45, 254
Li, 2018, Melanoma recognition in dermoscopy images via deep residual network, Chin. J. Biomed. Eng., 37, 274
Hu, 2019, Melano-ma classification method by integrating deep convolutional residual network, Comput. Sci., 46, 247
Liu, 2018, The diagnosis of Alzheimer's disease classification based on multi-scale residual neutral network, J. Shandong University (Engineering Sci-ence), 48, 1

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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
ORCID: Xu, Wanni
Fu, You-Lei
Zhu, Dongmei
Venue: Computer Methods and Programs in Biomedicine
Publisher: Elsevier BV
Published: 2023-10
Volume: 240
Pages: 107660
ISSN: 0169-2607 (ISSN-L)
DOI: 10.1016/j.cmpb.2023.107660
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: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
www.elsevier.com/legal/tdmrep-license (tdm)
doi.org/10.15223/policy-017 (stm-asf)
doi.org/10.15223/policy-037 (stm-asf)
doi.org/10.15223/policy-012 (stm-asf)
doi.org/10.15223/policy-029 (stm-asf)
doi.org/10.15223/policy-004 (stm-asf)
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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ResNet and its application to medical image processing: Research progress and challenges

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