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 Conference Paper Faculty Publications- Conference Papers 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 Joshi, Priyanka; Singh, Jagendra; Upreti, Kamal Title A name given to the resource Advancing Brain Tumor Detection with Deep Learning and Machine Learning: A Performance Analysis of Different Deep Learning Models 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 Lecture Notes in Networks and Systems;Volume;1359 LNNS;pp.279-289 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-981-96-4933-4_20" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-96-4933-4_20</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105020721047?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105020721047?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 Joshi P., School of Computer Science Engineering and Technology, Bennett University, Greater Noida, India; Singh J., School of Computer Science Engineering and Technology, Bennett University, Greater Noida, India; Upreti K., School of Computer Science, CHRIST University, New Delhi, India Description An account of the resource The current study examines the difficulty of employing a deep learning architecture to diagnose brain tumors quickly and effectively. Our study is built upon a dataset of 253 MRI pictures that have been carefully categorized by medical experts as either positive (Yes) or negative (No) for brain tumors. To guarantee the robustness of model performance, the dataset is carefully divided into training and validation subsets, with 70% set aside for training and 30% for validation. We analyze the diagnostic performance of several machine learning models, including K-Nearest Neighbors (KNNs), Recurrent Neural Networks (RNNs), Support Vector Machines (SVMs), Convolutional Neural Networks (CNNs), and Artificial Neural Networks (ANNs). When these algorithms are applied to MRI scans, brain tumors can be quickly detected, and the increased accuracy makes patient treatment easier. The findings of this study could lead to a rapid and accurate diagnosis of brain tumors, which would greatly enhance patient care and treatment. The results also show how deep learning frameworks can transform medical image processing and diagnosis. This work offers a thorough review of recent findings and techniques for MRI scan-based deep learning-based brain tumor detection. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026. Subject The topic of the resource Brain tumor; Deep learning; Heath care; Machine learning; Magnetic Resonance Imaging (MRI); Performance analysis Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Relation A related resource ISSN: 23673370; ISBN: 978-981964932-7; Language A language of the resource English Type The nature or genre of the resource Conference paper 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