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 Conference Papers 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/. Title A name given to the resource Brain Tumor Detectin Using Deep Learning Model Subject The topic of the resource Brain-Tumor detection; Deep Leaning; DenseNet201; Inception V3; MobileNet; ResNet50 Description An account of the resource Brain tumor is a life-threatening disease that can disrupt normal brain functioning and have a significant impact on a patient's quality of life. Early detection and diagnosis are crucial for effective treatment. In recent years, deep learning techniques for image analysis and detection have played a vital role in the medical field, supplying more accurate and reliable results. Segmentation, the process of distinguishing between normal and abnormal brain cells or tissues, is a critical step in the detection of brain tumors. In this research, we aim to investigate various techniques for brain tumor detection and segmentation using Magnetic Resonance Imaging (MRI) images. The detection process begins by analyzing the symmetric and asymmetric shape of the brain to identify abnormalities. We will then classify the cells as either Tumored or non-Tumored. This research is aimed at finding a more accurate and efficient method for detecting brain tumors. Four Keras models are compared side by side to find out the best deep learning model for providing a suitable outcome. The models are ResNet50, DenseNet201, Inception V3 and MobileNet. These models gave training accuracy of 85.30%, 78%, 78%, and 77.12% respectively. 2023 IEEE. Creator An entity primarily responsible for making the resource Menachery J.; Anand I.K.; Thiruthuvanathan M.M. Source A related resource from which the described resource is derived Proceedings of IEEE InC4 2023 - 2023 IEEE International Conference on Contemporary Computing and Communications Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. 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/10.1109/InC457730.2023.10262967" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/InC457730.2023.10262967</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85174740801&amp;doi=10.1109%2FInC457730.2023.10262967&amp;partnerID=40&amp;md5=33ce670f322684e20f30b60f21fa3830" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85174740801&amp;doi=10.1109%2fInC457730.2023.10262967&amp;partnerID=40&amp;md5=33ce670f322684e20f30b60f21fa3830</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISBN: 979-835033577-4 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 Conference paper Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Menachery J., Christ (Deemed to Be University), Department of Computer Science, Karnataka, Bengaluru, India; Anand I.K., Christ (Deemed to Be University), Department of Computer Science, Karnataka, Bengaluru, India; Thiruthuvanathan M.M., Christ (Deemed to Be University), Department of Computer Science, Karnataka, Bengaluru, India