Genetic Algorithm Based Optimization of Convolutional Neural Network for Respiratory Disease Detection

Vishwachetan D, Nandini S B, Pranjal Shrivastava, Nihal Jahagirdar

2025

Abstract

The pandemic Covid 19 in the year 2019 highlighted the need for advanced diagnostic methodologies to address a spectrum of pulmonary diseases. Although the major method of COVID-19 detection is still conventional PCR testing, the combination of AI and X-ray imaging presents a promising path toward a thorough diagnosis of pulmonary illness. Here, we provide a new optimization framework based on the Xception neural network architecture and Genetic Algorithm (GA) for precise pulmonary disease detection from X-ray pictures, including coronavirus and pneumonis (viral, bacterial). By utilising deep learning and convolutional neural networks, the main aim of this paper to improve the accuracy and efficiency of diagnosis. Using GA, we explore the vast design space of deep CNN architectures, encompassing parameters such as network depth, layer count, and type. Utilising an extensive dataset of X-ray pictures, the suggested Xception-based neural network is rigorously assessed repeatedly through GA-driven optimization. The result highlight how well the improved model distinguishes lung disorders achieved with AI-driven approaches.

Paper Citation

in Harvard Style

D V., S B N., Shrivastava P. and Jahagirdar N. (2025). Genetic Algorithm Based Optimization of Convolutional Neural Network for Respiratory Disease Detection. In Proceedings of the 3rd International Conference on Futuristic Technology - Volume 3: INCOFT; ISBN 978-989-758-763-4, SciTePress, pages 804-810. DOI: 10.5220/0013733200004664

in Bibtex Style

@conference{incoft25,
author={Vishwachetan D and Nandini S B and Pranjal Shrivastava and Nihal Jahagirdar},
title={Genetic Algorithm Based Optimization of Convolutional Neural Network for Respiratory Disease Detection},
booktitle={Proceedings of the 3rd International Conference on Futuristic Technology - Volume 3: INCOFT},
year={2025},
pages={804-810},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013733200004664},
isbn={978-989-758-763-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 3rd International Conference on Futuristic Technology - Volume 3: INCOFT
TI - Genetic Algorithm Based Optimization of Convolutional Neural Network for Respiratory Disease Detection
SN - 978-989-758-763-4
AU - D V.
AU - S B N.
AU - Shrivastava P.
AU - Jahagirdar N.
PY - 2025
SP - 804
EP - 810
DO - 10.5220/0013733200004664
PB - SciTePress