Transfer learning techniques for medical image analysis: A review
Article
Kora, Padmavathi, Ooi, Chui Ping, Faust, Oliver, Raghavendra, U., Gudigar, Anjan, Chan, Wai Yee, Meenakshi, K., Swaraja, K., Plawiak, Pawel and Acharya, U. Rajendra. 2022. "Transfer learning techniques for medical image analysis: A review." Biocybernetics and Biomedical Engineering. 42 (1), pp. 79-107. https://doi.org/10.1016/j.bbe.2021.11.004
| Article Title | Transfer learning techniques for medical image analysis: A review |
|---|---|
| ERA Journal ID | 211872 |
| Article Category | Article |
| Authors | Kora, Padmavathi, Ooi, Chui Ping, Faust, Oliver, Raghavendra, U., Gudigar, Anjan, Chan, Wai Yee, Meenakshi, K., Swaraja, K., Plawiak, Pawel and Acharya, U. Rajendra |
| Journal Title | Biocybernetics and Biomedical Engineering |
| Journal Citation | 42 (1), pp. 79-107 |
| Number of Pages | 29 |
| Year | 2022 |
| Publisher | Elsevier BV |
| Place of Publication | Netherlands |
| ISSN | 0208-5216 |
| 2391-467X | |
| Digital Object Identifier (DOI) | https://doi.org/10.1016/j.bbe.2021.11.004 |
| Web Address (URL) | https://www.sciencedirect.com/science/article/abs/pii/S0208521621001297 |
| Abstract | Medical imaging is a useful tool for disease detection and diagnostic imaging technology has enabled early diagnosis of medical conditions. Manual image analysis methods are labor-intense and they are susceptible to intra as well as inter-observer variability. Automated medical image analysis techniques can overcome these limitations. In this review, we investigated Transfer Learning (TL) architectures for automated medical image analysis. We discovered that TL has been applied to a wide range of medical imaging tasks, such as segmentation, object identification, disease categorization, severity grading, to name a few. We could establish that TL provides high quality decision support and requires less training data when compared to traditional deep learning methods. These advantageous properties arise from the fact that TL models have already been trained on large generic datasets and a task specific dataset is only used to customize the model. This eliminates the need to train the models from scratch. Our review shows that AlexNet, ResNet, VGGNet, and GoogleNet are the most widely used TL models for medical image analysis. We found that these models can understand medical images, and the customization refines the ability, making these TL models useful tools for medical image analysis. |
| Keywords | Convolutional neural networks; Machine learning; Transfer learning; Medical image |
| ANZSRC Field of Research 2020 | 400306. Computational physiology |
| Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
| Byline Affiliations | Gokaraju Rangaraju Institute of Engineering and Technology, India |
| Singapore University of Social Sciences (SUSS), Singapore | |
| Sheffield Hallam University, United Kingdom | |
| Manipal Academy of Higher Education, India | |
| University of Malaya, Malaysia | |
| Cracow University of Technology, Poland | |
| Polish Academy of Sciences, Poland | |
| Ngee Ann Polytechnic, Singapore | |
| Asia University, Taiwan | |
| Kumamoto University, Japan | |
| Library Services |
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