A Comprehensive Survey of Machine Learning Applications in Medical Image Analysis for Artificial Vision
DOI:
https://doi.org/10.33050/italic.v2i1.438Keywords:
Machine Learning, Medical Image Analysis, Artificial Vision , Comprehensive Survey, ApplicationsAbstract
This study presents a thorough survey of the applications of machine learning in medical image analysis for artificial vision,
aiming to offer a comprehensive understanding of the evolving intersection between machine learning and medical imaging.
With the rapid advancement of artificial vision technologies, the integration of machine learning algorithms has become
pivotal in revolutionizing medical image analysis. The survey explores a diverse range of machine learning applications
within the medical imaging domain, encompassing techniques such as convolutional neural networks (CNNs), support vector
machines, and decision trees. The focus lies in elucidating the role of machine learning in enhancing the accuracy, efficiency,
and diagnostic capabilities of medical image analysis systems. Key topics addressed in the survey include image segmentation, classification, and detection, with a specific emphasis on applications in radiology, pathology, and ophthalmology. Additionally, the survey discusses challenges and opportunities in the integration of machine learning into medical image analysis, providing insights into current trends and future directions. This comprehensive survey serves as a valuable resource for researchers, practitioners, and healthcare professionals seeking an in-depth overview of the diverse applications and evolving landscape of machine learning in medical image analysis for artificial vision.
References
Yao, X., Wang, X., Wang, S. H., & Zhang, Y. D. (2022). A comprehensive survey on
convolutional neural network in medical image analysis. Multimedia Tools and Applications,
(29), 41361-41405.
Elyan, E., Vuttipittayamongkol, P., Johnston, P., Martin, K., McPherson, K., Jayne, C., & Sarker,
M. K. (2022). Computer vision and machine learning for medical image analysis: recent
advances, challenges, and way forward. Artificial Intelligence Surgery, 2.
Atasever, S., Azginoglu, N., Terzi, D. S., & Terzi, R. (2023). A comprehensive survey of deep
learning research on medical image analysis with focus on transfer learning. Clinical Imaging, 94,
-41.
Barragán-Montero, A., Javaid, U., Valdés, G., Nguyen, D., Desbordes, P., Macq, B., ... & Lee, J.
A. (2021). Artificial intelligence and machine learning for medical imaging: A technology review.
Physica Medica, 83, 242-256.
Stolte, S., & Fang, R. (2020). A survey on medical image analysis in diabetic retinopathy.
Medical image analysis, 64, 101742.
Nahar, N., Hossain, M. S., & Andersson, K. (2022). Medical Image Analysis Using Machine
Learning and Deep Learning: A Comprehensive Review. Rhythms in Healthcare, 147-161.
Wang, J., Zhu, H., Wang, S. H., & Zhang, Y. D. (2021). A review of deep learning on medical
image analysis. Mobile Networks and Applications, 26, 351-380.
Rehman, A., Butt, M. A., & Zaman, M. (2021, April). A survey of medical image analysis using
deep learning approaches. In 2021 5th International Conference on Computing Methodologies
and Communication (ICCMC) (pp. 1334-1342). IEEE.
Chen, X., Wang, X., Zhang, K., Fung, K. M., Thai, T. C., Moore, K., ... & Qiu, Y. (2022). Recent
advances and clinical applications of deep learning in medical image analysis. Medical Image Analysis,
, 102444.
Sarvamangala, D. R., & Kulkarni, R. V. (2022). Convolutional neural networks in medical image
understanding: a survey. Evolutionary intelligence, 15(1), 1-22.
Jing, Y., Li, C., Du, T., Jiang, T., Sun, H., Yang, J., ... & Li, X. (2023). A comprehensive survey of
intestine histopathological image analysis using machine vision approaches. Computers in Biology and
Medicine, 107388.
Rana, M., & Bhushan, M. (2023). Machine learning and deep learning approach for medical image
analysis: diagnosis to detection. Multimedia Tools and Applications, 82(17), 26731-26769.
Faragallah, O. S., El-Hoseny, H., El-Shafai, W., Abd El-Rahman, W., El-Sayed, H. S., El-Rabaie, E. S.
M., ... & Geweid, G. G. (2020). A comprehensive survey analysis for present solutions of medical
image fusion and future directions. IEEE Access, 9, 11358-11371.
Bhattacharya, S., Maddikunta, P. K. R., Pham, Q. V., Gadekallu, T. R., Chowdhary, C. L., Alazab, M., &
Piran, M. J. (2021). Deep learning and medical image processing for coronavirus (COVID-19)
pandemic: A survey. Sustainable cities and society, 65, 102589.
Kazerouni, A., Aghdam, E. K., Heidari, M., Azad, R., Fayyaz, M., Hacihaliloglu, I., & Merhof, D.
(2022). Diffusion models for medical image analysis: A comprehensive survey. arXiv preprint
arXiv:2211.07804.
Setyowati, W., & Rahayu, I. S. (2023). Sector Analysis of Islamic Capital Markets and Artificial
Intelligence Functioning as Sharia Advisors. International Transactions on Artificial Intelligence, 1(2),
-244.
Prawiyogi, A. G., & Widayanti, R. (2023). Exploratory Activities in Educational Games using Fuzzy
Logic. International Transactions on Artificial Intelligence, 1(2), 188-194.
Anwar, M. R. (2023). Analysis of Expert System Implementation in Computer Damage Diagnosis with
Forward Chaining Method. International Transactions on Artificial Intelligence, 1(2), 139-155.
Purnama, S., & Sejati, W. (2023). Internet of Things, Big Data, and Artificial Intelligence in The Food
and Agriculture Sector. International Transactions on Artificial Intelligence, 1(2), 156-174.
Mariyanti, T. (2023). Development of Mobile Learning Applications for Android Based on Artificial
Intelligence. International Transactions on Artificial Intelligence, 1(2), 230-235.
Bhima, B., Zahra, A. R. A., Nurtino, T., & Firli, M. Z. (2023). Enhancing Organizational Efficiency
through the Integration of Artificial Intelligence in Management Information Systems. APTISI
Transactions on Management, 7(3), 282-289.
Sutrisna, S., Maulani, G., Anwar, M. R., Nurtino, T., & Bhima, B. (2022, November). FAST-Based
Production Monitoring Information System for Extra-High Voltage Power Line Towers. In 2022 IEEE
Creative Communication and Innovative Technology (ICCIT) (pp. 1-8). IEEE.
Bharati, S., Mondal, M., Podder, P., & Prasath, V. B. (2022). Deep learning for medical image
registration: A comprehensive review. arXiv preprint arXiv:2204.11341.
Singha, A., Thakur, R. S., & Patel, T. (2021). Deep learning applications in medical image analysis.
Biomedical Data Mining for Information Retrieval: Methodologies, Techniques and Applications,
-350.
Downloads
Published
Issue
Section
License
Copyright (c) 2023 Alwiyah
This work is licensed under a Creative Commons Attribution 4.0 International License.
