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 Sujana, D. Swainson; Augustine, D. Peter Title A name given to the resource FaithfulNet: An explainable deep learning framework for autism diagnosis using structural MRI 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 Brain Research;Volume;1866;Issue;;Article No.;149904; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1016/j.brainres.2025.149904" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.brainres.2025.149904</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105014541873?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105014541873?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 Sujana D.S., Department of Computer Science, Christ (Deemed to be University), Karnataka, Bangalore, 560 029, India; Augustine D.P., Department of Computer Science, Christ (Deemed to be University), Karnataka, Bangalore, 560 029, India Description An account of the resource Explainable Artificial Intelligence (XAI) can decode the black box models, enhancing trust in clinical decision-making. XAI makes the predictions of deep learning models interpretable, transparent, and trustworthy. This study employed XAI techniques to explain the predictions made by a deep learning-based model for diagnosing autism and identifying the memory regions responsible for children's academic performance. This study utilized publicly available sMRI data from the ABIDE-II repository. First, a deep learning model, FaithfulNet, was developed to aid in the diagnosis of autism. Next, gradient-based class activation maps and the SHAP gradient explainer were employed to generate explanations for the model's predictions. These explanations were integrated to develop a novel and faithful visual explanation, Faith_CAM. Finally, this faithful explanation was quantified using the pointing game score and analyzed with cortical and subcortical structure masks to identify the impaired brain regions in the autistic brain. This study achieved a classification accuracy of 99.74% with an AUC value of 1. In addition to facilitating autism diagnosis, this study assesses the degree of impairment in memory regions responsible for the children's academic performance, thus contributing to the development of personalized treatment plans. 2025 Elsevier B.V. Subject The topic of the resource Autism diagnosis; Grad_CAM; SHAP; sMRI; XAI Publisher An entity responsible for making the resource available Elsevier B.V. Relation A related resource ISSN: 68993; CODEN: BRREA 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 Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online