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 Reviews Review Faculty Publications- Reviews 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 A Thorough Review of Deep Learning in Autism Spectrum Disorder Detection: From Data to Diagnosis Subject The topic of the resource ABIDE dataset; ASGCN (adaptive spatiotemporal; atlases; autism spectrum disorder (ASD); comprehensive review; Deep learning; electroencephalography (EEG); graph convolution network); LeNet; neuroimaging modalities; preprocessing tools; resting-state functional magnetic resonance imaging (rsfMRI); structural MRI (sMRI); task-based fMRI (tfMRI) Description An account of the resource Background: Autism Spectrum Disorder (ASD) is a multifaceted neurodevelop-mental condition with significant heterogeneity in its clinical presentation. Timely and precise identification of ASD is crucial for effective intervention and assistance. Recent advances in deep learning techniques have shown promise in enhancing the accuracy of ASD detection. Objective: This comprehensive review aims to provide an overview of various deep learning methods employed in detecting ASD, utilizing diverse neuroimaging modalities. We analyze a range of studies that use resting-state functional Magnetic Resonance Imaging (rsfMRI), structural MRI (sMRI), task-based fMRI (tfMRI), and electroencephalography (EEG). This paper aims to assess the effectiveness of these techniques based on criteria such as accuracy, sensitiv-ity, specificity, and computational efficiency. Methods: We systematically review studies investigating ASD detection using deep learning across different neuroimaging modalities. These studies utilize various preprocessing tools, at-lases, feature extraction techniques, and classification algorithms. The performance metrics of interest include accuracy, sensitivity, specificity, precision, F1-score, recall, and area under the curve (AUC). Results: The review covers a wide range of studies, each with its own dataset and methodolo-gy. Notable findings include a study employing rsfMRI data from ABIDE that achieved an accuracy of 80% using LeNet. Another study using rsfMRI data from ABIDE-II achieved an im-pressive accuracy of 95.4% with the ASGCN deep learning model. Studies utilizing different modalities, such as EEG and sMRI, also reported high accuracies ranging from 74% to 95%. Conclusion: Deep learning-based approaches for ASD detection have demonstrated significant potential across multiple neuroimaging modalities. These methods offer a more objective and data-driven approach to diagnosis, potentially reducing the subjectivity associated with clinical evaluations. However, challenges remain, including the need for larger and more diverse da-tasets, model interpretability, and clinical validation. The field of deep learning in ASD diagnosis continues to evolve, holding promise for early and accurate identification of individuals with ASD, which is crucial for timely intervention and support. 2024 Bentham Science Publishers. Creator An entity primarily responsible for making the resource Lamani M.R.; Pernabas J.B. Source A related resource from which the described resource is derived Recent Advances in Computer Science and Communications, Vol-17, No. 8, pp. 73-91. Publisher An entity responsible for making the resource available Bentham Science Publishers 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/10.2174/0126662558284886240130154414" target="_blank" rel="noreferrer noopener">https://doi.org/10.2174/0126662558284886240130154414</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85200203016&amp;doi=10.2174%2F0126662558284886240130154414&amp;partnerID=40&amp;md5=07dc0ddc06e0de6f558286a4ad752df1" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85200203016&amp;doi=10.2174%2f0126662558284886240130154414&amp;partnerID=40&amp;md5=07dc0ddc06e0de6f558286a4ad752df1</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 26662558 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 Review Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Lamani M.R., Department of Computer Science and Engineering, CHRIST (Deemed to be University), Karnataka, Bengaluru, 560074, India; Pernabas J.B., Department of Computer Science and Engineering, CHRIST (Deemed to be University), Karnataka, Bengaluru, 560074, India