Transfer learning techniques for medical image analysis: A review

TitleTransfer learning techniques for medical image analysis: A review
Publication TypeJournal Article
Year of Publication2022
AuthorsKora P, Ooi CPing, Faust O, Raghavendra U., Gudigar A, Chan WYee, Meenakshi K., Swaraja K., Plawiak P, U. Acharya R
JournalBiocybernetics and Biomedical Engineering
Volume42
ISSN0208-5216
Keywordsconvolutional neural networks, Machine learning, Medical image, Transfer learning
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.

URLhttps://www.sciencedirect.com/science/article/pii/S0208521621001297
DOI10.1016/j.bbe.2021.11.004

Historia zmian

Data aktualizacji: 08/11/2022 - 15:56; autor zmian: Łukasz Zimny (lzimny@iitis.pl)