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 Articles 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/. Title A name given to the resource RF-ShCNN: A combination of two deep models for tumor detection in brain using MRI Subject The topic of the resource Adaptive wiener filter; Brain tumor; Conditional random fields; Deep residual network; Fractional calculus; MRI; Shepherd convolution neural network Description An account of the resource The tumor in the brain is the reason for jagged cell enlargement in the brain. Magnetic resonance imaging (MRI) is a common scheme to identify tumor existence in the brain. With these MRIs, the medical practitioner can examine and detect the abnormal growth of tissues and corroborate if the brain is influenced by a tumor or not. Due to the appearance of artificial intelligence models, the discovery of brain tumor is performed by adapting different models which thereby help in making decisions and selecting the most suitable diagnosis for patients. The main motivation of this work is to reduce the death rate. If they are not adequately treated, the survival rate of the patient decreases. The correct diagnoses help patients receive accurate treatments and survive for a long time. This paper develops a hybrid model, namely the Residual fused Shepherd convolution neural network (RF-ShCNN) for discovering tumor in the brain considering MRI. Thus, the Adaptive wiener filtering is adapted to filter image-commencing noise. Thereafter, Conditional Random Fields-Recurrent Neural Networks (CRF-RNN) are adapted for segmentation followed by the mining of essential features. Lastly, the features employed in RF-ShCNN for making effective brain tumor detection by means of MRI. Thus, the RF-ShCNN is built by unifying the deep residual network and Shepherd convolution neural network. The hybridization is done by adding a regression layer wherein the regression is fused with Fractional calculus (FC) to make effective detection. The RF-ShCNN provided better accuracy of 94%, sensitivity of 95% and specificity of 94.9%. 2023 Creator An entity primarily responsible for making the resource Balasubramanian S.; Mandala J.; Rao T.V.M.; Misra A. Source A related resource from which the described resource is derived Biomedical Signal Processing and Control, Vol-88 Publisher An entity responsible for making the resource available Elsevier Ltd 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/10.1016/j.bspc.2023.105656" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.bspc.2023.105656</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85175637596&amp;doi=10.1016%2Fj.bspc.2023.105656&amp;partnerID=40&amp;md5=08264d5aa9ab240b86f54848a3353e73" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85175637596&amp;doi=10.1016%2fj.bspc.2023.105656&amp;partnerID=40&amp;md5=08264d5aa9ab240b86f54848a3353e73</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 17468094 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 Article Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Balasubramanian S., Computer Science and Engineering, Jain (Deemed-to-be University), Faculty of Engineering and Technology, Ramanagar District, Karnataka, 562112, India; Mandala J., Department of Computer Science and Engineering, Christ (Deemed to be) University, Bangalore, India; Rao T.V.M., Department of Computer Science and Engineering, Vignan's Institute of Information Technology, Visakhapatnam, India; Misra A., School of Computer Application, Lovely Professional University, Jalandhar Delhi G.T. Road, Phagwara, Punjab, 144411, India