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 Vadivu, P. Balashanmuga; Prabhakar, Telagarapu; Mandala, Jyothi; Chandra Sekhar, Kattamuri Venkata Title A name given to the resource Hybrid Mobile-Spinalnet with feature extraction for brain tumor detection using MRI images Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived Biomedical Signal Processing and Control;Volume;112;Issue;;Article No.;108571; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1016/j.bspc.2025.108571" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.bspc.2025.108571</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105013884056?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105013884056?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 Vadivu P.B., Department of ECE, Mahendra Engineering College, Tamil Nadu, Namakkal, India; Prabhakar T., Department of ECE, GMR Institute of Technology, GMR Nagar, Andhra Pradesh, Rajam, India; Mandala J., Department of Computer science and engineering, CHRIST (Deemed to be University), Banglore, India; Chandra Sekhar K.V., Department of Artificial Intelligence &amp; Machine Learning, Aditya Institute of Technology and Management, Srikakulam, Tekkali, India Description An account of the resource Brain tumors are deadly and can hinder the normal functioning of the human body. Generally, surgical methods are preferred for treating brain tumors. Early and accurate detection remains a problem, due to the complexity of tumor shapes and poor generalization to diverse tumor types. To address this, a hybrid Mobile-SpinalNet is established in this paper for the detection of brain tumors with Magnetic Resonance Imaging (MRI) images. This system involves seven stages, including input image acquisition, image preprocessing, skill stripping, tumor segmentation, data augmentation, feature extraction, and brain tumor detection. Initially, image acquisition is carried out, and then the input image is preprocessed by using a Mean filter. Subsequently, the skull stripping is performed using Fuzzy C-Means (FCM). After that, by using the TransUNet, the tumor region is isolated in the segmentation module. Furthermore, the data augmentation is carried out, and then the feature mining takes place in the feature extraction phase to excerpt features such as Speeded Up Robust Features (SURF), Oriented Fast and Rotated BRIEF (ORB), Fuzzy Local Binary Pattern (FLBP) and statistical features. At last, the brain tumor is identified by employing a hybrid Mobile-SpinalNet. This framework fuses the MobileNet and SpinalNet depending on regression modeling with applied Fractional Calculus (FC). The Mobile-SpinalNet is validated for its efficacy by comparing it to other techniques, and it showed better performance with a precision of 0.953, accuracy of 0.943, and recall of 0.970. 2025 Elsevier Ltd Subject The topic of the resource Brain tumor; Fractional Calculus; Fuzzy C-Means; Hybrid model; MobileNet; SpinalNet; TransUNet Publisher An entity responsible for making the resource available Elsevier Ltd Relation A related resource ISSN: 17468094; 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