Abstract: The use of machine learning (ML) in medical image analysis has shown great promise in aiding clinical decision making and improving patient outcomes. The ability of ML algorithms to automatically detect patterns and features in medical images has led to advancements in diagnosis, prognosis, and treatment planning. In this paper, we review the state-of-the-art techniques in ML for medical image analysis, including supervised, unsupervised, and deep learning methods. We discuss the challenges and opportunities in the field, such as data privacy concerns, the need for large annotated datasets, and the interpretability of ML models. We also provide examples of successful applications of ML in medical imaging, such as the detection of tumors in mammograms and the segmentation of brain structures in MRI scans. Finally, we discuss the future directions of ML in medical image analysis, including the integration of multimodal data and the use of reinforcement learning for personalized treatment planning. Overall, ML has the potential to revolutionize medical imaging by providing more accurate and efficient diagnostic tools and improving patient outcomes.
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DOI:
10.17148/IJARCCE.2022.115218
