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dc.contributor.authorAndleeb, Ifrah
dc.contributor.authorHussain, B. Zahid
dc.contributor.authorAnsari, Salik
dc.contributor.authorAnsari, Mohammad Samar
dc.contributor.authorKanwal, Nadia
dc.contributor.authorAslam, Asra
dc.date.accessioned2024-02-07T15:39:51Z
dc.date.available2024-02-07T15:39:51Z
dc.date.issued2024-02-01
dc.identifierhttps://chesterrep.openrepository.com/bitstream/handle/10034/628476/Samar%20-%20Deep%20learning.pdf?sequence=1
dc.identifier.citationAndleeb, I., Hussain, B. Z., Ansari, S., Ansari, M. S., Kanwal, N., & Aslam, A. (2024). Deep learning based lightweight model for brain tumor classification and segmentation. In N. Naik, P. Jenkins, P. Grace, L. Yang, & S. Prajapat (Eds.) Advances in Computational Intelligence Systems (pp. 491–503). Springer. https://doi.org/10.1007/978-3-031-47508-5_38en_US
dc.identifier.isbn9783031475078en_US
dc.identifier.doi10.1007/978-3-031-47508-5_38
dc.identifier.urihttp://hdl.handle.net/10034/628476
dc.descriptionThis version of the contribution has been accepted for publication, after peer review (when applicable) but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: http://dx.doi.org/10.1007/978-3-031-47508-5_38]. Use of this Accepted Version is subject to the publisher’s Accepted Manuscript terms of use https://www.springernature.com/gp/open-research/policies/accepted-manuscript-terms.
dc.description.abstractThis paper presents two lightweight deep learning models for efficient detection and segmentation of brain tumors from MRI scans. A custom-made Convolutional Neural Network (CNN) is designed for identification of four different classes of brain tumors viz. Meningioma, Glioma, Pituitary brain tumor and normal (no tumor). Furthermore, another tailor-made lightweight model is presented for the segmentation of the tumor from the Magnetic Resonance Imaging (MRI) scans. The output of the segmentation model is the ‘mask’ depicting the tumor region. The overall performance in terms of detection accuracy, and segmentation accuracy, for the two models is found to be approximately 95% for both the cases individually. The proposed models are worthy additions to the existing literature on brain tumor classification and segmentation models due to their low-parameter count which make the models amenable for deployment on resource-constrained edge hardware.en_US
dc.publisherSpringeren_US
dc.relation.ispartofseriesAdvances in Intelligent Systems and Computing; 1453en_US
dc.relation.urlhttps://link.springer.com/chapter/10.1007/978-3-031-47508-5_38en_US
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.subjectBrain tumor detectionen_US
dc.subjectBrain tumor segmentationen_US
dc.subjectConvolution Neural Networken_US
dc.subjectDeep learningen_US
dc.subjectLightweight modelsen_US
dc.titleDeep Learning Based Lightweight Model for Brain Tumor Classification and Segmentationen_US
dc.typeConference Contributionen_US
dc.contributor.departmentAligarh Muslim University; University of Chester; Keele University; University of Leedsen_US
or.grant.openaccessYesen_US
rioxxterms.funderunfundeden_US
rioxxterms.identifier.projectunfundeden_US
rioxxterms.versionAMen_US
rioxxterms.versionofrecord10.1007/978-3-031-47508-5_38en_US
rioxxterms.licenseref.startdate2025-02-01
dcterms.dateAccepted2023-07-01
rioxxterms.publicationdate2024-02-01
dc.date.deposited2024-02-07en_US


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