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Medical images classification using deep learning: a survey

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Abstract

Deep learning has made significant advancements in recent years. The technology is rapidly evolving and has been used in numerous automated applications with minimal loss. With these deep learning methods, medical image analysis for disease detection can be performed with minimal errors and losses. A survey of deep learning-based medical image classification is presented in this paper. As a result of their automatic feature representations, these methods have high accuracy and precision. This paper reviews various models like CNN, Transfer learning, Long short term memory, Generative adversarial networks, and Autoencoders and their combinations for various purposes in medical image classification. The total number of papers reviewed is 158. In the study, we discussed the advantages and limitations of the methods. A discussion is provided on the various applications of medical imaging, the available datasets for medical imaging, and the evaluation metrics. We also discuss the future trends in medical imaging using artificial intelligence.

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Data Availability

Data sharing not applicable to this article as no datasets were generated during the current study. The datasets which used in this study their references are given in Table 7.

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    Rakesh Kumar, Pooja Kumbharkar, Sandeep Vanam & Sanjeev Sharma

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Kumar, R., Kumbharkar, P., Vanam, S. et al. Medical images classification using deep learning: a survey. Multimed Tools Appl 83, 19683–19728 (2024). https://doi.org/10.1007/s11042-023-15576-7

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