Real-Time Implementation of Deep Learning Model for Polyp Classification and Segmentation in Medical Imaging

Title

Real-Time Implementation of Deep Learning Model for Polyp Classification and Segmentation in Medical Imaging

Creator

Singhal, Prateek; Yadav, Rakesh Kumar

Description

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.

Source

Proceedings of the 2025 12th International Conference on Computing for Sustainable Global Development, INDIACom 2025;

Date

01-01-2025

Publisher

Institute of Electrical and Electronics Engineers Inc.

Subject

colonoscopy; Deep learning models; medical imaging; polyp classification; polyp detection; polyp segmentation; preliminary tests

Coverage

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

Rights

Restricted Access; Hardcopy may be available in the library

Relation

ISBN: 978-938054460-1;

Format

online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.23919/INDIACom66777.2025.11115652

https://www.scopus.com/pages/publications/105016230225?origin=resultslist