Xiamen Academy of Arts and Design, Fuzhou University, Xiamen 361021, China.
Department of Computer Information Engineering, Nanchang Institute of Technology, Nanchang 330044, China.
Comput Methods Programs Biomed. 2023 Oct;240:107660. doi: 10.1016/j.cmpb.2023.107660. Epub 2023 Jun 8.
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.
深度学习是机器学习的一种新方法和分支，近年来受到计算机视觉研究人员的广泛关注。这种方法之所以引起了极大的兴趣，是因为它具有非凡的医学图像解释能力，尤其是与残差神经网络结合使用时，这有助于推动该领域的发展。
本文对残差网络进行了以下研究。首先，介绍了 ResNet 在医学领域的研究现状。然后解释了残差神经网络的基本思想，以及残差单元、其多种结构和网络架构。其次，讨论了残差神经网络在医学图像处理中的四个广泛应用方面：肺肿瘤、皮肤病诊断、乳腺癌诊断和脑部疾病诊断。最后，讨论了处理医学图像中残差网络的主要问题和未来发展。
在医学图处理领域，残差神经网络在肺肿瘤、乳腺癌、皮肤病、心脑血管疾病等严重疾病的临床辅助诊断方面取得了长足的进步和成功。
我们全面梳理了残差神经网络研究的最新进展及其在医学图像处理中的应用，为该研究领域提供了重要的参考依据。通过总结 ResNet 模型在医学图像处理领域的应用现状和问题，并提出一些未来的发展方向，为进一步推动 ResNet 模型在医学图像处理领域的应用和研究提供了有益的参考。
