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Recent Optimization Methods and Techniques for Medical Image Analysis
Version 1
: Received: 28 September 2023 / Approved: 29 September 2023 / Online: 30 September 2023 (17:58:32 CEST)
How to cite: wang, J. Recent Optimization Methods and Techniques for Medical Image Analysis. Preprints 2023, 2023092137. https://doi.org/10.20944/preprints202309.2137.v1 wang, J. Recent Optimization Methods and Techniques for Medical Image Analysis. Preprints 2023, 2023092137. https://doi.org/10.20944/preprints202309.2137.v1
Abstract
Medical image analysis is an important branch in the field of medicine, which mainly uses image processing and analysis techniques to interpret and diagnose medical image data. Medical image data helps doctors to effectively observe and diagnose patients' body structures, tissues and lesions. Medical image analysis has been an important research area in the medical field, and it is important for disease diagnosis, treatment planning, and condition monitoring. In recent years, the rapid development of deep learning and computer vision technologies has contributed greatly to the automation, multimodal data fusion, real-time application, and accuracy improvement of medical image analysis. In addition, the development of deep learning has given rise to some new research areas in medical image analysis, such as Generative Adversarial Networks (GANs) for synthetic medical images, self-supervised learning for unsupervised feature learning, and neural network interpretability. In this paper, we will introduce some optimisation methods for medical images which are effective in improving the accuracy, efficiency and reliability of medical image analysis.
Keywords
Medical image analysis, Medical image data, Deep learning, Computer vision techniques, Optimisation methods
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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