Deep learning has come to be one of the most transformative generations in current clinical imaging, imparting advanced answers for diagnosis, segmentation, and sickness class. This complete evaluation explores the most essential deep learning architectures—inclusive of convolutional neural networks (CNNs), recurrent neural networks (RNNs), and deep perception networks (DBNs)—and their packages in the course of diverse imaging modalities, such as CT, MRI, PET, and fundus images. The examine highlights how deep studying complements accuracy, overall performance, and automation in scientific photograph interpretation. Moreover, it discusses the cutting-edge annoying conditions handling these structures, at the side of confined categorized information, immoderate computational needs, and the shortage of interpretability. The compare concludes with future commands emphasizing explainable AI, hybrid models, records augmentation, and federated analyzing as pathways to triumph over present boundaries and enhance real-international scientific packages.
Cite this paper
Ahmed, M. R. (2025). Deep Learning in Medical Imaging: A Comprehensive Review of Techniques, Challenges, and Future Directions
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