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A review of deep learning approaches in clinical and healthcare systems based on medical image analysis

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Abstract

Healthcare is a high-priority sector where people expect the highest levels of care and service, regardless of cost. That makes it distinct from other sectors. Due to the promising results of deep learning in other practical applications, many deep learning algorithms have been proposed for use in healthcare and to solve traditional artificial intelligence issues. The main objective of this study is to review and analyze current deep learning algorithms in healthcare systems. In addition, it highlights the contributions and limitations of recent research papers. It combines deep learning methods with the interpretability of human healthcare by providing insights into deep learning applications in healthcare solutions. It first provides an overview of several deep learning models and their most recent developments. It then briefly examines how these models are applied in several medical practices. Finally, it summarizes current trends and issues in the design and training of deep neural networks besides the future direction in this field.

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Data sharing does not apply to this article as no datasets were generated or analyzed during the current study.

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Authors and Affiliations

  1. Electrical Engineering Department, Faculty of Engineering, Damietta University, Damietta, Egypt

    Hadeer A. Helaly

  2. Computers and Control Systems Engineering Department, Faculty of Engineering, Mansoura University, Mansoura, Egypt

    Hadeer A. Helaly, Mahmoud Badawy & Amira Y. Haikal

  3. Department of Computer Science and Informatics, Applied College, Taibah University, Al Madinah Al Munawwarah, 41461, Saudi Arabia

    Mahmoud Badawy

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Helaly, H.A., Badawy, M. & Haikal, A.Y. A review of deep learning approaches in clinical and healthcare systems based on medical image analysis. Multimed Tools Appl 83, 36039–36080 (2024). https://doi.org/10.1007/s11042-023-16605-1

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