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 Praghash, K.; Chalasani, Vidyadhari; Balan, Santhosh Kumar; Daniya, T. Title A name given to the resource Pediatric brain tumor segmentation and classification framework using SGC-U-NET and ARC-DEEP-CNN 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 International Journal of Information Technology (Singapore); Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/s41870-025-02966-3" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/s41870-025-02966-3</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105025465649?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105025465649?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 Praghash K., Department of Electronics and Communication Engineering, Christ University, Bengaluru, India; Chalasani V., Department of Information Technology, Gokaraju Rangaraju Institute of Engineering and Technology, Bachupally, Hyderabad, India; Balan S.K., Department of Computer Science and Engineering, Guru Nanak Institute of Technology, Ranga Reddy (District), Ibrahimpatnam, Telangana, 501506, India; Daniya T., Department of CSE (AI &amp; ML), GMR Institute of Technology, Rajam, Andhra Pradesh, Vizianagaram, India Description An account of the resource Timely and precise pediatric Brain Tumor (BT) classification is challenging in the prevailing studies owing to the lack of growth rate calculation. Therefore, this paper proposes a growth rate-aware intelligent BT classification using child Magnetic Resonance Imaging (MRI) based on Arcsin Deep Convolutional Neural Network (Arc-Deep-CNN). Initially, the child's MRI is collected and then pre-processed for angle correction, resolution improvement, skull removal, and edge sharpening to improve the image quality. Meanwhile, the binary image dilation is done in the postpre-processing for accurate tumor location identification using the Central Limit Theorem-based Battle Royale Optimization Algorithm (CLT-BROA). From the pre-processed images, the wavelet features are extracted to improve the detection rate. Based on the tumor-identified images, pre-processed images, and extracted features, a robust Shuffled Group Convolutional layer added U-Net (SGC-U-Net) significantly segments the normal brain, benign, core, and malignant tumors affected brain. Then, the 3D tumor reconstruction is done by performing splitting, feature extraction, and growth rate calculation. Finally, a novel Arc-Deep-CNN proficiently classifies the BT into Medulloblastoma, Glioma, and Meningioma tumors with respect to the growth rate. The proposed Arc-Deep-CNN achieved maximum accuracy and minimum training time of 98.77% and 52136ms, respectively, showing impressive performance in pediatric BT classification. Bharati Vidyapeeth's Institute of Computer Applications and Management 2025. Subject The topic of the resource Arcsin deep convolutional neural network (Arc-Deep-CNN); Brain tumor; Central limit theorem-based battle royale optimization algorithm (CLT-BROA); Magnetic resonance imaging (MRI); Medulloblastoma; Shuffled group convolutional layer added U-net (SGC-U-Net) Publisher An entity responsible for making the resource available Springer Science and Business Media B.V. Relation A related resource ISSN: 25112104; 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