review · Information
Deep convolutional neural networks have transformed medical image analysis through the automated learning of hierarchical features from complex imaging data. Their development and distinct network architectures support clinical tasks across a wide variety of medical specialisms. These specialisms include oncology, neurology, cardiology, pulmonology, ophthalmology, dermatology, and orthopaedics. Alongside evaluating performance across these disciplines, key technical and operational challenges unique to medical imaging remain critical considerations. Emerging trends and prospective research trajectories indicate ongoing refinement in how these computational models process clinical imagery. Synthesising architectural progressions and domain-specific obstacles provides an essential foundation for researchers and practitioners working at the intersection of healthcare and artificial intelligence.
Medical imaging generates vast quantities of intricate visual data that require precise interpretation. Applying advanced neural networks allows healthcare systems to automate and improve feature detection across critical medical specialisms, from cancer detection to heart and brain imaging. Understanding the current technical architectures and hurdles helps clinicians and computer scientists develop safer, more reliable diagnostic support tools.
This work relates to diagnostic decision-support software and automated imaging tools intended for healthcare practitioners, radiologists, and clinical artificial intelligence developers. Because the work outlines a broad review covering architectural evolution, hurdles, and future research across multiple clinical fields, the underlying technology represents early-stage to applied research, with commercial deployment dependent on overcoming domain-specific imaging challenges.
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Deep convolutional neural networks (CNNs) have revolutionized medical image analysis by enabling the automated learning of hierarchical features from complex medical imaging datasets. This review provides a focused analysis of CNN evolution and architectures as applied to medical image analysis, highlighting their application and performance in different medical fields, including oncology, neurology, cardiology, pulmonology, ophthalmology, dermatology, and orthopedics. The paper also explores challenges specific to medical imaging and outlines trends and future research directions. This review aims to serve as a valuable resource for researchers and practitioners in healthcare and artificial intelligence.
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DOI: 10.3390/info16030195
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