A Comprehensive Review of Machine Learning Techniques in Medical Image Analysis for Disease Diagnosis
1 Department of Biomedical Engineering, University of Health Sciences, City, Country
2 AI Research Institute, City, Country
3 Radiology Department, General Hospital, City, Country
Corresponding Author:Dr. Anya Sharma
Medical image analysis plays a crucial role in the diagnosis and treatment of various diseases. With the advent of artificial intelligence, machine learning (ML) techniques have revolutionized this field, offering powerful tools for automated and accurate interpretation of complex medical images. This review provides a comprehensive overview of the application of diverse machine learning techniques, including supervised, unsupervised, and deep learning approaches, in medical image analysis. We discuss their fundamental principles, common architectures, and specific applications across different imaging modalities such as MRI, CT, X-ray, and ultrasound. Key challenges, such as data scarcity, interpretability, and generalization, are highlighted, along with potential solutions and future directions. The aim is to provide researchers and practitioners with a clear understanding of the current state-of-the-art, emerging trends, and the potential of ML to enhance diagnostic accuracy and efficiency in healthcare.
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How to Cite
Sharma, D. A., Carter, P. B., & Davis, D. C. (2013). A Comprehensive Review of Machine Learning Techniques in Medical Image Analysis for Disease Diagnosis. International Journal of Ayurvedic and Herbal Medicine, 3(6). https://doi.org/10.47191/ijahm/v3i6.03
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