Author(s): Suman Mehta* and Amar Nath Chatterjee
With the rise of deep learning, medical image analysis has undergone fundamental change, presenting new possibilities to physicians in terms of achieving higher accuracy more efficiently, and greater automation in disease diagnosis and prognosis. In this paper, we discuss recent progress in utilizing deep learning techniques such as convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), and transformer-based model for medical image analysis. Image classification, segmentation, and anomaly detection in modalities such MRI, CT, Xray, and ultrasound have been strongly improved by these architectures. Yet, such progress brings challenges, namely scarcity of sufficient data, interpretability of models, computational intensity, and robust generalization to diverse clinical datasets. At the same time regulatory concerns and ethical questions on the use of AI diagnostics are still constraining large scale adoption. This paper provides a comprehensive review of state-of-the art deep learning models in medical imaging, reviews the current limitations, and envisions future research directions in federated learning, explainable AI, and quantum enhanced deep learning. Such insights are intended to guide researchers and clinicians in the optimization of AI driven medical image analysis for optimized patient care and clinical decision making.
