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 Automated Brain Imaging Diagnosis and Classification Model using Rat Swarm Optimization with Deep Learning based Capsule Network Subject The topic of the resource Brain tumor; capsule network; Deep learning; image segmentation; medical imaging; Rat swarm optimizer Description An account of the resource Earlier identification of brain tumor (BT) is essential to increase the survival rate of the patients. The commonly used imaging technique for BT diagnosis is magnetic resonance imaging (MRI). Automated BT classification model is required for assisting the radiologists to save time and enhance efficiency. The classification of BT is difficult owing to the non-uniform shapes of tumors and location of tumors in the brain. Therefore, deep learning (DL) models can be employed for the effective identification, prediction, and diagnosis of diseases. In this view, this paper presents an automated BT diagnosis using rat swarm optimization (RSO) with deep learning based capsule network (DLCN) model, named RSO-DLCN model. The presented RSO-DLCN model involves bilateral filtering (BF) based preprocessing to enhance the quality of the MRI. Besides, non-iterative grabcut based segmentation (NIGCS) technique is applied to detect the affected tumor regions. In addition, DLCN model based feature extractor with RSO algorithm based parameter optimization processes takes place. Finally, extreme learning machine with stacked autoencoder (ELM-SA) based classifier is employed for the effective classification of BT. For validating the BT diagnostic performance of the presented RSO-DLCN model, an extensive set of simulations were carried out and the results are inspected under diverse dimensions. The simulation outcome demonstrated the promising results of the RSO-DLCN model on BT diagnosis with the sensitivity of 98.4%, specificity of 99%, and accuracy of 98.7%. 2023 World Scientific Publishing Company. Creator An entity primarily responsible for making the resource Vasantharaj A.; Rani P.S.; Huque S.; Raghuram K.S.; Ganeshkumar R.; Shafi S.N. Source A related resource from which the described resource is derived International Journal of Image and Graphics, Vol-23, No. 3 Publisher An entity responsible for making the resource available World Scientific Date A point or period of time associated with an event in the lifecycle of the resource 2023-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1142/S0219467822400010" target="_blank" rel="noreferrer noopener">https://doi.org/10.1142/S0219467822400010</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85110292625&amp;doi=10.1142%2FS0219467822400010&amp;partnerID=40&amp;md5=288247278eeda665816d07b5d3f84619" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85110292625&amp;doi=10.1142%2fS0219467822400010&amp;partnerID=40&amp;md5=288247278eeda665816d07b5d3f84619</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 2194678 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 Vasantharaj A., Department of ECE, Excel Engineering College (Autonomous), Namakkal Tamil Nadu, India; Rani P.S., Department of CSE, R.M.D Engineering College, Tamil Nadu, Chennai, India; Huque S., Department of CSE, Guru Nanak Institutions Technical Campus, Telangana, Hyderabad, India; Raghuram K.S., Department of Mechanical Engineering, Vignan's Institute of Information Technology(A), Andhra Pradesh, Visakhapatnam, India; Ganeshkumar R., Department of CSE, School of Engineering and Technology, CHRIST (Deemed to be University), Kengeri Campus, Karnataka, Bangalore, India; Shafi S.N., Department of Computer Science, Institute of Technology, Woldia University, Ethiopia