Scaling of Machine Learning Techniques in Medical Imagining and Biomedical Applications Concerning Healthcare

Year : 2024 | Volume :15 | Issue : 01 | Page : 41-44

Bhupinder Singh

  1. Professor Sharda School of Law, Sharda University Uttar Pradesh India


Machine 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, computed 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.

Keywords: Biomedical imagining, pattern recognition, machine learning, health, medical

[This article belongs to Journal of Computer Technology & Applications(jocta)]

How to cite this article: Bhupinder Singh. Scaling of Machine Learning Techniques in Medical Imagining and Biomedical Applications Concerning Healthcare. Journal of Computer Technology & Applications. 2024; 15(01):41-44.
How to cite this URL: Bhupinder Singh. Scaling of Machine Learning Techniques in Medical Imagining and Biomedical Applications Concerning Healthcare. Journal of Computer Technology & Applications. 2024; 15(01):41-44. Available from:


  1. 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. Phys Med. 2021; 83: 9–24.
  2. Zemouri R, Zerhouni N, Racoceanu D. Deep learning in the biomedical applications: recent and future status. Appl Sci. 2019; 9 (8): 1526.
  3. Singh B. 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. Rev Brasil Alternat Dispute Resolution – Brazil J Alternat Dispute Resolution. 2023; 5 (10): 81–105.
  4. 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. Proc IEEE. 2021; 109 (5): 820–838.
  5. Singh B, Kaunert C. 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 J. 2024; 6 (1): 13–20.
  6. Erickson BJ, Korfiatis P, Akkus Z, Kline TL. Machine learning for medical imaging. Radiographics. 2017; 37 (2): 505–515.
  7. Singh B. Tele-health monitoring lensing deep neural learning structure: ambient patient wellness via wearable devices for real-time alerts and interventions. Indian J Health Med Law. 2023; 6 (2): 12–16.
  8. 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. Phys Med. 2021; 83: 242–256.
  9. Singh B. Legal dynamics lensing metaverse crafted for videogame industry and e-sports: phenomenological exploration catalyst complexity and future. J Intellect Property Rights Law. 2024; 7 (1): 8–14.
  10. Singh B. Blockchain technology in renovating healthcare: legal and future perspectives. In: Kaushik K, Dahiya S, Aggarwal S, Dwivedi AD. Revolutionizing Healthcare Through Artificial Intelligence and Internet of Things Applications. Hershey, PA, USA: IGI Global; 2023. pp. 177–186.
  11. Qayyum A, Qadir J, Bilal M, Al-Fuqaha A. Secure and robust machine learning for healthcare: a survey. IEEE Rev Biomed Eng. 2020; 14: 156–180.
  12. Singh B. 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). Natl J Environ Law. 2023; 6 (2): 6–17.
  13. Sharma A, Singh B. Measuring impact of e-commerce on small scale business: a systematic review. J Corporate Govern Int Business Law. 2023; 5 (1): 34–38.
  14. 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. Lancet Digital Health. 2019; 1 (6): e271–e297.
  15. Singh B. Understanding legal frameworks concerning transgender healthcare in the age of dynamism. Electron J Soc Strategic Stud. 2022; 3: 56–65.
  16. 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 Biol Med. 2022; 145: 105458.
  17. Singh B. Relevance of agriculture-nutrition linkage for human healthcare: a conceptual legal framework of implication and pathways. Justice Law Bull. 2022; 1 (1): 44–49.
  18. Ker J, Wang L, Rao J, Lim T. Deep learning applications in medical image analysis. IEEE Access. 2017; 6: 9375–9389.
  19. Singh, B. COVID-19 pandemic and public healthcare: endless downward spiral or solution via rapid legal and health services implementation with patient monitoring program. Justice Law Bull. 2022; 1 (1): 1–7.
  20. Chen YW, Jain LC. Deep Learning in Healthcare: Paradigms and Applications. Heidelberg, Germany: Springer; 2020.
  21. Singh B. Global science and jurisprudential approach concerning healthcare and illness. Indian J Health Med Law. 2020; 3 (1): 7–13.
  22. Ravì D, Wong C, Deligianni F, Berthelot M, Andreu-Perez J, Lo B, Yang GZ. Deep learning for health informatics. IEEE J Biomed Health Inform. 2016; 21 (1): 4–21.
  23. Singh B. Profiling public healthcare: a comparative analysis based on the multidimensional healthcare management and legal approach. Indian J Health Med Law. 2019; 2 (2): 1–5.
  24. Greenspan H, Van Ginneken B, Summers RM. Guest editorial deep learning in medical imaging: overview and future promise of an exciting new technique. IEEE Trans Med Imaging. 2016; 35 (5): 1153–1159.
  25. 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. J Biomed Inform. 2021; 113: 103627.

Regular Issue Subscription Review Article
Volume 15
Issue 01
Received March 16, 2024
Accepted March 29, 2024
Published April 5, 2024