Applications of Deep Learning in Medical Image Analysis: a Comprehensive Review of Recent Advances and Challenges

Medical imaging serves as a cornerstone in modern healthcare, enabling non-invasive diagnosis, treatment planning, and patient follow-up through modalities like X-rays, CT scans, MRI, ultrasound, and PET scans. This comprehensive review delves into the transformative impact of deep learning on the field, mentioning core methodologies like CNNs, RNNs, Autoencoders, GANs, ViTs, and hybrid models. Applications are dissected across clinical domains including oncology, neurology, cardiology, pulmonology, ophthalmology, dermatology, and orthopedics, alongside tasks ranging from classification and segmentation to detection, reg- istration, reconstruction, synthesis, and forecasting. Achievements are highlighted, particularly in achieving superior accuracy for classification and precise delineation in segmentation across varied medical contexts.