Shobayo Olamilekan, Saatchi Reza
School of Engineering and Built Environment, Sheffield Hallam University, Pond Street, Sheffield S1 1WB, UK.
School of Computing and Digital Technologies, Sheffield Hallam University, 151 Arundel Street, Sheffield S1 2NU, UK.
Diagnostics (Basel). 2025 Apr 23;15(9):1072. doi: 10.3390/diagnostics15091072.
Deep learning has revolutionised medical image analysis, offering the possibility of automated, efficient, and highly accurate diagnostic solutions. This article explores recent developments in deep learning techniques applied to medical imaging, including convolutional neural networks (CNNs) for classification and segmentation, recurrent neural networks (RNNs) for temporal analysis, autoencoders for feature extraction, and generative adversarial networks (GANs) for image synthesis and augmentation. Additionally, U-Net models for segmentation, vision transformers (ViTs) for global feature extraction, and hybrid models integrating multiple architectures are explored. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) process were used, and searches on PubMed, Google Scholar, and Scopus databases were conducted. The findings highlight key challenges such as data availability, interpretability, overfitting, and computational requirements. While deep learning has demonstrated significant potential in enhancing diagnostic accuracy across multiple medical imaging modalities-including MRI, CT, US, and X-ray-factors such as model trust, data privacy, and ethical considerations remain ongoing concerns. The study underscores the importance of integrating multimodal data, improving computational efficiency, and advancing explainability to facilitate broader clinical adoption. Future research directions emphasize optimising deep learning models for real-time applications, enhancing interpretability, and integrating deep learning with existing healthcare frameworks for improved patient outcomes.
深度学习彻底改变了医学图像分析，提供了自动化、高效且高度准确的诊断解决方案的可能性。本文探讨了应用于医学成像的深度学习技术的最新进展，包括用于分类和分割的卷积神经网络（CNN）、用于时间分析的循环神经网络（RNN）、用于特征提取的自动编码器以及用于图像合成和增强的生成对抗网络（GAN）。此外，还探讨了用于分割的U-Net模型、用于全局特征提取的视觉Transformer（ViT）以及集成多种架构的混合模型。使用了系统评价和荟萃分析的首选报告项目（PRISMA）流程，并在PubMed、谷歌学术和Scopus数据库上进行了检索。研究结果突出了数据可用性、可解释性、过拟合和计算需求等关键挑战。虽然深度学习在提高包括MRI、CT、US和X射线在内的多种医学成像模态的诊断准确性方面已显示出巨大潜力，但模型信任、数据隐私和伦理考量等因素仍然是持续关注的问题。该研究强调了整合多模态数据、提高计算效率和推进可解释性以促进更广泛临床应用的重要性。未来的研究方向强调为实时应用优化深度学习模型、增强可解释性以及将深度学习与现有医疗保健框架集成以改善患者预后。
