A survey on deep learning in medical image analysis

Deep learning has rapidly transformed deep learning medical imaging by enabling automated extraction of complex patterns from large imaging datasets. Medical image analysis plays a critical role in disease diagnosis, treatment planning, and monitoring, yet traditional methods often rely on handcrafted features and significant expert intervention. Recently, AI in medical imaging, particularly CNN medical imaging approaches such as convolutional neural networks, has achieved strong performance in image classification, segmentation, and detection across multiple modalities. These techniques are widely applied in radiology, pathology, ophthalmology, and cardiology, improving diagnostic accuracy and clinical efficiency.To communicate these advances clearly, researchers increasingly use AI in medical imaging visualization, including medical image analysis graphical abstract formats and research graphical abstract design. Support from scientific graphical abstract service providers also enables clear medical AI illustration for academic publications. Despite progress, challenges remain in data availability, model generalizability, and clinical integration.