Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Kumar, Senthil; Ramprasath, J.; Kalpana, V.; Rajagopal, Manikandan; S, Maheswaran; Gupta, Rupesh Title A name given to the resource Integrating brain-inspired computation with big-data analytics for advanced diagnostics in neuroradiology Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Neuroscience Informatics;Volume;5;Issue;2;Article No.;100202; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1016/j.neuri.2025.100202" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.neuri.2025.100202</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105004811471?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105004811471?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant 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&amp;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 Description An account of the resource 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 Subject The topic of the resource Advanced imaging; Big-data analytics; Brain-inspired computation; Diagnostic framework; Machine learning; Neuroradiology Publisher An entity responsible for making the resource available Elsevier Masson s.r.l. Relation A related resource ISSN: 27725286; Language A language of the resource English Type The nature or genre of the resource Article Rights Information about rights held in and over the resource All Open Access; Gold Open Access; Green Open Access Format The file format, physical medium, or dimensions of the resource online