LLRBFNN Deep Learning Model-Based Digital Twin Framework for Detecting Breast Cancer

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Author(s): B. Srinivasulu (BVRIT HYDERABAD College of Engineering for Women, India), Srinivasa Rao Dhanikonda (BVRIT HYDERABAD College of Engineering for Women, India), Aruna Rao S. L. (BVRIT HYDERABAD College of Engineering for Women, India), Ravikumar Mutyala (Stanley College of Engineering and Technology for Women, India), Mukhtar Ahmad Sofi (BVRIT HYDERABAD College of Engineering for Women, India)and Obula Reddy Bandi (Independent Researcher, UK)
Copyright: 2024
Pages: 14
Source title: Exploring the Advancements and Future Directions of Digital Twins in Healthcare 6.0
Source Author(s)/Editor(s): Archi Dubey (The ICFAI University, India), C. Kishor Kumar Reddy (Stanley College of Engineering and Technology for Women, India), Srinath Doss (Botho University, Botswana)and Marlia Mohd Hanafiah (Universiti Kebangsaan Malaysia, Malaysia)
DOI: 10.4018/979-8-3693-5893-1.ch015

Keywords: Computer Science & IT / Computer Simulation / Healthcare Administration / Medical Information Science Reference

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Abstract

This study proposes a digital twin framework for healthcare training and diagnostics, using a patient model to identify and group mammo graphic wounds based on the site of examination. The framework uses a local linear radial basis function neural network (LLRBFNN) deep learning model, fuzzy c-means calculations, and beneficial nervous system characterization. The methodology combines surface highlight images and conditions to detect and group malignant breast tumor growth. The study aims to improve strategies for identifying different classes of breast disorders.

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LLRBFNN Deep Learning Model-Based Digital Twin Framework for Detecting Breast Cancer

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