计算机科学
人工智能
学习迁移
医学影像学
分割
深度学习
机器学习
医学诊断
分类
计算机视觉
模式识别(心理学)
医学
放射科
作者
Padmavathi Kora,Chui Ping Ooi,Oliver Faust,U. Raghavendra,Anjan Gudigar,Wai Yee Chan,K. Meenakshi,K. Swaraja,Paweł Pławiak,U. Rajendra Acharya
标识
DOI:10.1016/j.bbe.2021.11.004
摘要
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.
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