Integrating brain-inspired computation with big-data analytics for advanced diagnostics in neuroradiology
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...
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Elsevier
2025-06-01
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| Series: | Neuroscience Informatics |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772528625000172 |
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| author | Senthil Kumar J. Ramprasath V. Kalpana Manikandan Rajagopal Maheswaran S Rupesh Gupta |
| author_facet | Senthil Kumar J. Ramprasath V. Kalpana Manikandan Rajagopal Maheswaran S Rupesh Gupta |
| author_sort | Senthil Kumar |
| collection | DOAJ |
| 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. |
| format | Article |
| id | doaj-art-6196247cb62f461f9826ee95db3a6e5a |
| institution | OA Journals |
| issn | 2772-5286 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Neuroscience Informatics |
| spelling | doaj-art-6196247cb62f461f9826ee95db3a6e5a2025-08-20T02:26:20ZengElsevierNeuroscience Informatics2772-52862025-06-015210020210.1016/j.neuri.2025.100202Integrating brain-inspired computation with big-data analytics for advanced diagnostics in neuroradiologySenthil Kumar0J. Ramprasath1V. Kalpana2Manikandan Rajagopal3Maheswaran S4Rupesh Gupta5Department of Information Technology, Karpagam Academy of Higher Education, Deemed to be University, Eachanari, Coimbatore −641021, India; Corresponding author.Department of Information Technology, Dr. Mahalingam College of Engineering and Technology, Pollachi, IndiaDepartment of Computer Science and Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Avadi, Chennai, IndiaSchool of Business and Management, Christ university, Bangalore, IndiaDepartment of Electronics and Communication Engineering, Kongu Engineering College, Erode, IndiaChitkara University Institute of Engineering and Technology, Chitkara University, Punjab, IndiaIntroduction: 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.http://www.sciencedirect.com/science/article/pii/S2772528625000172Brain-inspired computationBig-data analyticsNeuroradiologyDiagnostic frameworkMachine learningAdvanced imaging |
| spellingShingle | Senthil Kumar J. Ramprasath V. Kalpana Manikandan Rajagopal Maheswaran S Rupesh Gupta Integrating brain-inspired computation with big-data analytics for advanced diagnostics in neuroradiology Neuroscience Informatics Brain-inspired computation Big-data analytics Neuroradiology Diagnostic framework Machine learning Advanced imaging |
| title | Integrating brain-inspired computation with big-data analytics for advanced diagnostics in neuroradiology |
| title_full | Integrating brain-inspired computation with big-data analytics for advanced diagnostics in neuroradiology |
| title_fullStr | Integrating brain-inspired computation with big-data analytics for advanced diagnostics in neuroradiology |
| title_full_unstemmed | Integrating brain-inspired computation with big-data analytics for advanced diagnostics in neuroradiology |
| title_short | Integrating brain-inspired computation with big-data analytics for advanced diagnostics in neuroradiology |
| title_sort | integrating brain inspired computation with big data analytics for advanced diagnostics in neuroradiology |
| topic | Brain-inspired computation Big-data analytics Neuroradiology Diagnostic framework Machine learning Advanced imaging |
| url | http://www.sciencedirect.com/science/article/pii/S2772528625000172 |
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