A Survey of Medical Image Analysis Based on Machine Learning Techniques

  • Ruaa Jasim Al Gharrawi Al-Furat Al-Awsat Technical University (ATU), Engineering Technical College of Al-Najaf, Department of Communication Engineering, Iraq,
  • Alyaa Abdulhussein Al-Joda Al-Furat Al-Awsat Technical University (ATU), Engineering Technical College of Al-Najaf, Department of Communication Engineering, Iraq
Keywords: Medical image, machine learning, deep learning, neural networks


Machine learning is a result of the availability and accessibility of a massive amount of data collected via sensors and the internet. The concept of machine learning demonstrates and spreads the fact that computers can improve themselves. Deep learning is causing a paradigm shift in medical image analysis. A medical image is a visual representation of the interior of a body, typically used for diagnostic or therapeutic purposes. Researchers and policymakers interested in healthcare outcomes should read this; this research provides an overview of machine learning at a high level. Computer vision is the field of using computer algorithms to understand and analyze visual data, and machine learning is a key tool for developing these algorithms. This review discovered that there are three varieties of machine learning strategies: supervised, unsupervised, and semi-supervised, and they seem to be gaining traction in risk assessment, disease prognosis, and image-based diagnosis, with increasing success. Convolutional neural networks (CNNs), k-means clustering, random forests, transductive learning, and support vector machines are among the most commonly used algorithms. Image analysis using CNN is the most effective method for medical imaging.


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How to Cite
Al Gharrawi, R., & Al-Joda, A. (2023). A Survey of Medical Image Analysis Based on Machine Learning Techniques. Journal of Al-Qadisiyah for Computer Science and Mathematics, 15(1), Comp Page 48-67. https://doi.org/10.29304/jqcm.2023.15.1.1139
Computer article