Deep learning on medical image analysis
CAAI Transactions on Intelligence Technology, ISSN: 2468-2322, Vol: 10, Issue: 1, Page: 1-35
2025
- 1Citations
- 27Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Metrics Details
- Citations1
- Citation Indexes1
- CrossRef1
- Captures27
- Readers27
- 27
Review Description
Medical image analysis plays an irreplaceable role in diagnosing, treating, and monitoring various diseases. Convolutional neural networks (CNNs) have become popular as they can extract intricate features and patterns from extensive datasets. The paper covers the structure of CNN and its advances and explores the different types of transfer learning strategies as well as classic pre-trained models. The paper also discusses how transfer learning has been applied to different areas within medical image analysis. This comprehensive overview aims to assist researchers, clinicians, and policymakers by providing detailed insights, helping them make informed decisions about future research and policy initiatives to improve medical image analysis and patient outcomes.
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