ItemArticle

Integrating brain-inspired computation with big-data analytics for advanced diagnostics in neuroradiology

Title
Integrating brain-inspired computation with big-data analytics for advanced diagnostics in neuroradiology
Creator
Kumar, Senthil; Ramprasath, J.; Kalpana, V.; Rajagopal, Manikandan; S, Maheswaran; Gupta, Rupesh
Description
Introduction: Neuroradiology encounters considerable difficulties owing to imaging data's intricacy and high-dimensional characteristics. Conventional diagnostic techniques often encounter challenges regarding precision and scalability, resulting in delays and possible misinterpretations. This paper presents the Big-Data Analytics-based Diagnostics (BDA-D) framework, a revolutionary method using computational models derived from neural architectures and sophisticated analytics to tackle these difficulties. Methods: The BDA-D architecture utilizes data mining, pattern recognition, and machine learning to glean useful neuroanatomical characteristics from massive datasets. By simulating human thought processes, this method speeds up clinical decision-making and improves diagnostic accuracy. To evaluate the effectiveness of the framework, it is put to the test in a clinical environment. Results and Discussion: Diagnostic precision, processing speed, and dependability are all enhanced by experimental validation. By detecting even the most minute neuroanatomical changes, BDA-D allows for more accurate diagnosis than traditional approaches. Based on the results, neuroradiologists may improve their practices by using cutting-edge computational methods to close the gap between data-driven analytics and their practical use in the clinic. BDA-D discovers important patterns from high-dimensional neuroimaging data through biologically inspired neural networks, reaching a remarkable diagnosis accuracy of 97.18%. Its 95.42% increase in processing speed allows rapid study of important disorders such as strokes and neurodegenerative diseases. BDA-D reduces inter-observer variability with a dependable value of 94.96%, increasing clinical confidence in AI-assisted diagnosis. Conclusion: A revolutionary change in neurodiagnostics, the BDA-D framework improves efficiency and reliability. This method has the potential to completely transform neuroradiology by combining big-data analytics with sophisticated computer models. It will allow for more rapid and precise diagnosis. 2025 The Authors
Source
Neuroscience Informatics;Volume;5;Issue;2;Article No.;100202;
Date
01-01-2025
Publisher
Elsevier Masson s.r.l.
Subject
Advanced imaging; Big-data analytics; Brain-inspired computation; Diagnostic framework; Machine learning; Neuroradiology
Coverage
Kumar S., Department of Information Technology, Karpagam Academy of Higher Education, Deemed to be University, Eachanari, Coimbatore, ?641021, India; Ramprasath J., Department of Information Technology, Dr. Mahalingam College of Engineering and Technology, Pollachi, India; Kalpana V., Department of Computer Science and Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Avadi, Chennai, India; Rajagopal M., School of Business and Management, Christ university, Bangalore, India; S M., Department of Electronics and Communication Engineering, Kongu Engineering College, Erode, India; Gupta R., Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, India
Rights
All Open Access; Gold Open Access; Green Open Access
Relation
ISSN: 27725286;
Format
online
Language
English
Type
Article

Citation

Kumar, Senthil; Ramprasath, J.; Kalpana, V.; Rajagopal, Manikandan; S, Maheswaran; Gupta, Rupesh, “Integrating brain-inspired computation with big-data analytics for advanced diagnostics in neuroradiology,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 19, 2026, https://archives.christuniversity.in/items/show/22417.

Output Formats