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 Articles 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/. Title A name given to the resource Normalized group activations based feature extraction technique using heterogeneous data for Alzheimers disease classification Subject The topic of the resource Alzheimers disease; Classification; Deep learning; Feature extraction; Functional connectivity; Normalized group activations Description An account of the resource Several deep learning networks are developed to identify the complex atrophic patterns of Alzheimers disease (AD). Among various activation functions used in deep neural networks, the rectifier linear unit is the most used one. Even though these functions are analyzed individually, group activations and their interpretations are still not explored for neuroimaging analysis. In this study, a unique feature extraction technique based on normalized group activations that can be applied to both structural MRI and resting-state-fMRI (rs-fMRI) is proposed. This method is split into two phases: multi-trait condensed feature extraction networks and regional association networks. The initial phase involves extracting features from various brain regions using different multi-layered convolutional networks. Then, multiple regional association networks with normalized group activations for all the regional pairs are trained and the output of these networks is given as input to a classifier. To provide an unbiased estimate, an automated diagnosis system equipped with the proposed feature extraction is designed and analyzed on multi-cohort Alzheimers Disease Neuroimaging Initiative (ADNI) data to predict multi-stages of AD. This system is also trained/tested on heterogeneous features such as non-transformed features, curvelets, wavelets, shearlets, textures, and scattering operators. Baseline scans of 185 rs-fMRIs and 1442 MRIs from ADNI-1, ADNI-2, and ADNI-GO datasets are used for validation. For MCI (mild cognitive impairment) classifications, there is an increase of 14% in performance. The outcome demonstrates the good discriminatory behaviour of the proposed features and its efficiency on rs-fMRI time-series and MRI data to classify multiple stages of AD. 2024 Vaithianathan et al. Creator An entity primarily responsible for making the resource Vaithianathan K.; Pernabas J.B.; Parthiban L.; Rashid M.; Alshamrani S.S. Source A related resource from which the described resource is derived PeerJ Computer Science, Vol-10, pp. 1-30. Publisher An entity responsible for making the resource available PeerJ Inc. Date A point or period of time associated with an event in the lifecycle of the resource 2024-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.7717/peerj-cs.2502" target="_blank" rel="noreferrer noopener">https://doi.org/10.7717/peerj-cs.2502</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85210748150&amp;doi=10.7717%2Fpeerj-cs.2502&amp;partnerID=40&amp;md5=2b7c7d736f15fc18c8b55a1280610bc3" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85210748150&amp;doi=10.7717%2fpeerj-cs.2502&amp;partnerID=40&amp;md5=2b7c7d736f15fc18c8b55a1280610bc3</a> Rights Information about rights held in and over the resource All Open Access; Gold Open Access Relation A related resource ISSN: 23765992 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Article Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Vaithianathan K., Department of Computer Engineering, Karaikal Polytechnic College, Karaikal, Puducherry, Varichikudy, India; Pernabas J.B., Department of Computer Science and Engineering, CHRIST (Deemed to be University, Kengeri Campus, Karnataka, India; Parthiban L., Department of Computer Science and Engineering, Community College, Pondicherry University, Puducherry, India; Rashid M., School of Information Communication and Technology, Bahrain Polytechnic, Isa Town, Bahrain; Alshamrani S.S., Department of Information Technology, College of Computers and Information Technology, Taif University, Taif, Saudi Arabia