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Open Access
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Bhupinder Singh
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- Professor, Sharda School of Law, Sharda University, Uttar Pradesh, India
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Abstract
nMachine learning refers to a field within computer science enabling computers to learn without explicit programming. Stemming from artificial intelligence’s study of pattern recognition and computational learning theory, machine learning develops algorithms capable of learning from vast datasets and making predictions. Its applications span diverse computing tasks like email filtering, network intrusion detection, optical character recognition, and computer vision, where conventional algorithm design proves challenging. Notably, in computer vision, a subset of computer science, machine learning plays a pivotal role. It addresses various challenges such as image recognition, object detection, and medical image processing, leveraging advancements in computing and imaging technologies. The growing complexity of biomedical data underscores the need for precise machine learning algorithms in biomedical engineering research. The fast progress of technology has a significant influence on medical science, especially in the field of imaging diagnostics. For example, computer tomography makes it possible to view interior human organs and tissues non-invasively, obviating the need for surgery. This encourages research into new, reliable, and more efficient diagnostic and treatment methods. Medical imaging, which includes biomedical signal capture, is becoming more and more important not only for therapy, monitoring the effectiveness of treatments, and rehabilitation procedures, but also for diagnosis. The growing amounts of data produced by medical diagnostic equipment make it more difficult for clinicians to manually explore and analyze the data. This paper explores the applications of machine learning in medical imagining and biomedical applications in healthcare.
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Keywords: Biomedical Imagining, Pattern Recognition, Machine Learning, Health, Medical
n[if 424 equals=”Regular Issue”][This article belongs to Journal of Computer Technology & Applications(jocta)]
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References
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- Castiglioni I, Rundo L, Codari M, Di Leo G, Salvatore C, Interlenghi M, Gallivanone F, Cozzi A, D’Amico NC, Sardanelli F. AI applications to medical images: From machine learning to deep learning. Physica medica. 2021 Mar 1;83:9-24.
- Zemouri R, Zerhouni N, Racoceanu D. Deep learning in the biomedical applications: Recent and future status. Applied Sciences. 2019 Apr 12;9(8):1526.
- Singh, B. (2023). Unleashing Alternative Dispute Resolution (ADR) in Resolving Complex Legal-Technical Issues Arising in Cyberspace Lensing E-Commerce and Intellectual Property: Proliferation of E-Commerce Digital Economy. Revista Brasileira de Alternative Dispute Resolution-Brazilian Journal of Alternative Dispute Resolution-RBADR, 5(10), 81-105.
- Zhou SK, Greenspan H, Davatzikos C, Duncan JS, Van Ginneken B, Madabhushi A, Prince JL, Rueckert D, Summers RM. A review of deep learning in medical imaging: Imaging traits, technology trends, case studies with progress highlights, and future promises. Proceedings of the IEEE. 2021 Feb 26;109(5):820-38.
- Singh, B., & Kaunert, C. (2024). Integration of Cutting-Edge Technologies such as Internet of Things (IoT) and 5G in Health Monitoring Systems: A Comprehensive Legal Analysis and Futuristic Outcomes. GLS Law Journal, 6(1), 13-20.
- Erickson BJ, Korfiatis P, Akkus Z, Kline TL. Machine learning for medical imaging. radiographics. 2017 Mar;37(2):505-15.
- Singh, B. (2023). Tele-Health Monitoring Lensing Deep Neural Learning Structure: Ambient Patient Wellness via Wearable Devices for Real-Time Alerts and Interventions. Indian Journal of Health and Medical Law, 6(2), 12-16.
- Barragán-Montero A, Javaid U, Valdés G, Nguyen D, Desbordes P, Macq B, Willems S, Vandewinckele L, Holmström M, Löfman F, Michiels S. Artificial intelligence and machine learning for medical imaging: A technology review. Physica Medica. 2021 Mar 1;83:242-56.
- Singh, B. (2024). Legal Dynamics Lensing Metaverse Crafted for Videogame Industry and E-Sports: Phenomenological Exploration Catalyst Complexity and Future. Journal of Intellectual Property Rights Law, 7(1), 8-14.
- Singh, B. (2023). Blockchain Technology in Renovating Healthcare: Legal and Future Perspectives. In Revolutionizing Healthcare Through Artificial Intelligence and Internet of Things Applications(pp. 177-186). IGI Global.
- Qayyum A, Qadir J, Bilal M, Al-Fuqaha A. Secure and robust machine learning for healthcare: A survey. IEEE Reviews in Biomedical Engineering. 2020 Jul 31;14:156-80.
- Singh, B. (2023). Federated Learning for Envision Future Trajectory Smart Transport System for Climate Preservation and Smart Green Planet: Insights into Global Governance and SDG-9 (Industry, Innovation and Infrastructure). National Journal of Environmental Law, 6(2), 6-17.
- Sharma, A., & Singh, B. (2022). Measuring Impact of E-commerce on Small Scale Business: A Systematic Review. Journal of Corporate Governance and International Business Law, 5(1).
- Liu X, Faes L, Kale AU, Wagner SK, Fu DJ, Bruynseels A, Mahendiran T, Moraes G, Shamdas M, Kern C, Ledsam JR. A comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging: a systematic review and meta-analysis. The lancet digital health. 2019 Oct 1;1(6):e271-97.
- Singh, B. (2022). Understanding Legal Frameworks Concerning Transgender Healthcare in the Age of Dynamism. ELECTRONIC JOURNAL OF SOCIAL AND STRATEGIC STUDIES, 3, 56-65.
- Shehab M, Abualigah L, Shambour Q, Abu-Hashem MA, Shambour MK, Alsalibi AI, Gandomi AH. Machine learning in medical applications: A review of state-of-the-art methods. Computers in Biology and Medicine. 2022 Jun 1;145:105458.
- Singh, B. (2022). Relevance of Agriculture-Nutrition Linkage for Human Healthcare: A Conceptual Legal Framework of Implication and Pathways. Justice and Law Bulletin, 1(1), 44-49.
- Ker J, Wang L, Rao J, Lim T. Deep learning applications in medical image analysis. Ieee Access. 2017 Dec 29;6:9375-89.
- Singh, B. (2022). COVID-19 Pandemic and Public Healthcare: Endless Downward Spiral or Solution via Rapid Legal and Health Services Implementation with Patient Monitoring Program. Justice and Law Bulletin, 1(1), 1-7.
- Chen YW, Jain LC. Deep learning in healthcare. Paradigms and Applications; Springer: Berlin/Heidelberg, Germany. 2020.
- Singh, B. (2020). GLOBAL SCIENCE AND JURISPRUDENTIAL APPROACH CONCERNING HEALTHCARE AND ILLNESS. Indian Journal of Health and Medical Law, 3(1), 7-13.
- Ravì D, Wong C, Deligianni F, Berthelot M, Andreu-Perez J, Lo B, Yang GZ. Deep learning for health informatics. IEEE journal of biomedical and health informatics. 2016 Dec 29;21(1):4-21.
- Singh, B. (2019). Profiling Public Healthcare: A Comparative Analysis Based on the Multidimensional Healthcare Management and Legal Approach. Indian Journal of Health and Medical Law, 2(2), 1-5.
- Greenspan H, Van Ginneken B, Summers RM. Guest editorial deep learning in medical imaging: Overview and future promise of an exciting new technique. IEEE transactions on medical imaging. 2016 Apr 29;35(5):1153-9.
- Shamshirband S, Fathi M, Dehzangi A, Chronopoulos AT, Alinejad-Rokny H. A review on deep learning approaches in healthcare systems: Taxonomies, challenges, and open issues. Journal of Biomedical Informatics. 2021 Jan 1;113:103627.
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| Volume | 15 | |
| [if 424 equals=”Regular Issue”]Issue[/if 424][if 424 equals=”Special Issue”]Special Issue[/if 424] [if 424 equals=”Conference”][/if 424] | 01 | |
| Received | March 16, 2024 | |
| Accepted | March 29, 2024 | |
| Published | April 5, 2024 |
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