Machine Learning in Medical Image Analysis: A Review of Techniques and Applications

Medical image analysis is now an indispensable part of modern healthcare that not only provides an opportunity to conduct non-invasive diagnostics, plan treatment, and monitor disease progress but also makes this a possibility. The swiftly growing imaging technologies like Magnetic Resonance Imaging (MRI), Computed Tomography (CT), X-ray, and Ultrasound have generated huge quantities of multidimensional images that have put unnecessary pressure on manual interpretation. Deep learning (DL) and machine learning (ML) have come to be one of the most significant paradigms of automation and enhancement of the analysis of such images. In this review, the current application of ML to medical imaging is described. It explains basic ML methods, such as classical supervised and unsupervised techniques, and modern DL models (Convolutional Neural Networks [CNNs], Generative Adversarial Networks [GANs], and Recurrent Neural Networks [RNNs]) to discuss their application in clinical contexts, such as detecting, segmenting, registering, and planning the treatment of diseases. The benchmark datasets (LUNA16, BRATS, and CheXpert) and standard evaluation metrics (accuracy, precision, recall, F1-score, AUC-ROC, and Dice similarity coefficient) are also discussed. Some key barriers are distinguished, such as access and quality of data, model interpretability, computational requirements, and regulatory and ethical concerns. New areas like multimodal data fusion, explainable AI, federated learning to capture privacy-saving analytics, and real-time edge computing are also pointed out.