Kora, Padmavathi and Ooi, Chui Ping and Faust, Oliver and Raghavendra, U. and Gudigar, Anjan and Chan, Wai Yee and Meenakshi, K. and Swaraja, K. and 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. ISSN 0208-5216, DOI https://doi.org/10.1016/j.bbe.2021.11.004.
Full text not available from this repository.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. Auto-mated 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 Goo-gleNet are the most widely used TL models for medical image analysis. We found that these
| Item Type: | Article |
|---|---|
| Funders: | None |
| Uncontrolled Keywords: | Medical image; Machine learning; Convolutional neural networks; Transfer learning |
| Subjects: | R Medicine T Technology > TA Engineering (General). Civil engineering (General) |
| Divisions: | Faculty of Medicine > Biomedical Imaging Department |
| Depositing User: | Ms. Juhaida Abd Rahim |
| Date Deposited: | 28 Aug 2023 08:28 |
| Last Modified: | 27 Nov 2024 01:47 |
| URI: | http://eprints.um.edu.my/id/eprint/43094 |
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