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 Singhal, Prateek; Yadav, Rakesh Kumar Title A name given to the resource Real-Time Implementation of Deep Learning Model for Polyp Classification and Segmentation in Medical Imaging Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Proceedings of the 2025 12th International Conference on Computing for Sustainable Global Development, INDIACom 2025; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.23919/INDIACom66777.2025.11115652" target="_blank" rel="noreferrer noopener">https://doi.org/10.23919/INDIACom66777.2025.11115652</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105016230225?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105016230225?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 Singhal P., Maharishi University of Information Technology, Department of Computer Science and Engineering, Lucknow, India, School of Sciences, Christ (Deemed to be University), Department of Computer Science, Delhi-NCR, India; Yadav R.K., Maharishi University of Information Technology, Department of Computer Science and Engineering, Lucknow, India Description An account of the resource Real-time deep learning models for polyp identification and segmentation in medical imaging. Recognising the limits of current database systems for real-time applications, the research focusses on creating a deep learning model capable of recognising crucial picture components to aid in precise polyp categorisation. The suggested methodology is intended for realtime, practical healthcare and diagnostic applications that need quick polyp detection via preliminary colonoscopy testing. Performance investigation demonstrates that ResNet50 and EfficientNet B2 outperform other models, implying that they are suitable for real-world application and optimal outcomes. 2025 Bharati Vidyapeeth, New Delhi. Subject The topic of the resource colonoscopy; Deep learning models; medical imaging; polyp classification; polyp detection; polyp segmentation; preliminary tests Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 978-938054460-1; 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