Transfer learning techniques for medical image analysis: a review
Authors:
- Padmavathi Kora,
- Chui Ping Ooi,
- Oliver Faust,
- U. Raghavendra,
- Anjan Gudigar,
- Wai Yee Chan,
- K. Meenakshi,
- K. Swaraja,
- Paweł Pławiak,
- U. Rajendra Acharya
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.
- Record ID
- CUT61ff381e3bd34bd7b1306a08288497aa
- Publication categories
- ;
- Author
- Journal series
- Biocybernetics and Biomedical Engineering, ISSN 0208-5216
- Issue year
- 2022
- Vol
- 42
- No
- 1
- Pages
- 79-107
- Other elements of collation
- fot.; rys.; tab.; wykr.; Bibliografia (na s.) - 100-107; Bibliografia (liczba pozycji) - 265; Oznaczenie streszczenia - Abstr.; Data udostępnienia on-line - 2021-12-13; Numeracja w czasopiśmie - Vol. 42, Iss. 1
- Keywords in English
- medical image, machine learning, convolutional neural networks, transfer learning
- ASJC Classification
- DOI
- DOI:10.1016/j.bbe.2021.11.004 Opening in a new tab
- URL
- https://www.sciencedirect.com/science/article/pii/S0208521621001297?via%3Dihub Opening in a new tab
- Language
- eng (en) English
- Score (nominal)
- 140
- Score source
- journalList
- Score
- Publication indicators
- Citation count
- 151
- Additional fields
- Indeksowana w: Scopus
- Uniform Resource Identifier
- https://cris.pk.edu.pl/info/article/CUT61ff381e3bd34bd7b1306a08288497aa/
- URN
urn:pkr-prod:CUT61ff381e3bd34bd7b1306a08288497aa
* presented citation count is obtained through Internet information analysis, and it is close to the number calculated by the Publish or PerishOpening in a new tab system.