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<p>Medical image analysis is essential for precise diagnosis, optimal treatment planning, non-invasive monitoring, anomaly detection, advancing medical research, improving patient outcomes, and enhancing healthcare efficiency. Conventional medical image analysis techniques are constrained by manual effort, subjectivity, scalability limitations, and less sophisticated feature extraction, whereas deep learning methods offer automated, consistent, and adaptable capabilities. The integration of deep learning in medical image analysis has delivered substantial improvements, with studies reporting up to 95% accuracy in disease detection, outperforming traditional methods by a margin of 10–15%. This article provides a comprehensive review of the diverse deep learning methods applied to medical image analysis, encompassing disease diagnosis, image segmentation, and image enhancement. It explores recent innovations and developments, highlighting significant advancements and addressing current challenges. By analyzing recent progress and discussing future trends, this review article aims to offer valuable insights for researchers and practitioners, facilitating the development of advanced deep learning solutions in medical imaging and ultimately enhancing diagnostic capabilities.</p>

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Advances in Deep Learning for Medical Image Analysis: A Comprehensive Investigation

  • 期刊论文

  • 发表日期：

    2025年1月23日

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  • Rajeev Ranjan Kumar,
  • S. Vishnu Shankar,
  • Ronit Jaiswal,
  • Mrinmoy Ray,
  • Neeraj Budhlakoti,
  • K. N. Singh

摘要

Medical image analysis is essential for precise diagnosis, optimal treatment planning, non-invasive monitoring, anomaly detection, advancing medical research, improving patient outcomes, and enhancing healthcare efficiency. Conventional medical image analysis techniques are constrained by manual effort, subjectivity, scalability limitations, and less sophisticated feature extraction, whereas deep learning methods offer automated, consistent, and adaptable capabilities. The integration of deep learning in medical image analysis has delivered substantial improvements, with studies reporting up to 95% accuracy in disease detection, outperforming traditional methods by a margin of 10–15%. This article provides a comprehensive review of the diverse deep learning methods applied to medical image analysis, encompassing disease diagnosis, image segmentation, and image enhancement. It explores recent innovations and developments, highlighting significant advancements and addressing current challenges. By analyzing recent progress and discussing future trends, this review article aims to offer valuable insights for researchers and practitioners, facilitating the development of advanced deep learning solutions in medical imaging and ultimately enhancing diagnostic capabilities.