01942nas a2200241   4500000000100000000000100001008004100002100002000043700001300063700001700076700001900093700001800112700001300130700001700143700001500160700001800175700002400193245007000217856007200287490000700359520132000366022001401686       2022                            d1 aPadmavathi Kora1 aChui Ooi1 aOliver Faust1 aU. Raghavendra1 aAnjan Gudigar1 aWai Chan1 aK. Meenakshi1 aK. Swaraja1 aPawel Plawiak1 aU. Rajendra Acharya00aTransfer learning techniques for medical image analysis: A review  uhttps://www.sciencedirect.com/science/article/pii/S02085216210012970 v423 a<p>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.</p>
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