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Background: DCNNs have led to revolution of medical imaging for disease diagnosis, segmentation and prognosis. Such application has brought undeniable gains in accuracy on diverse clinical domains. Objective: This study offers a systematic review of the applicability, challenges, and proposed work directions in medical imaging in DCNNs. Methods: PubMed, IEEE Xplore, Scopus and Google Scholar were searched in a comprehensive manner, and the studies were deemed relevant, methodologically rigorous and performance metrics based. Results: Overall, DCNNs have resulted in much higher diagnostic accuracy when used for tumour classification, lung disease detection, brain lesion segmentation and cardiac abnormality identification. However, there are still a few challenges, including the issue of scarcity of data, the lack of the model interpretability and the clinical adoption.

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Title: Advances, Challenges, and Future Directions of Deep Convolutional Neural Networks in Medical Imaging: A Systematic Review

  • 会议论文

  • 发表日期：

    2026年

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  • Hetal M. Bhatt,
  • Sunil Bajeja

摘要

Background: DCNNs have led to revolution of medical imaging for disease diagnosis, segmentation and prognosis. Such application has brought undeniable gains in accuracy on diverse clinical domains. Objective: This study offers a systematic review of the applicability, challenges, and proposed work directions in medical imaging in DCNNs. Methods: PubMed, IEEE Xplore, Scopus and Google Scholar were searched in a comprehensive manner, and the studies were deemed relevant, methodologically rigorous and performance metrics based. Results: Overall, DCNNs have resulted in much higher diagnostic accuracy when used for tumour classification, lung disease detection, brain lesion segmentation and cardiac abnormality identification. However, there are still a few challenges, including the issue of scarcity of data, the lack of the model interpretability and the clinical adoption.