Improving COVID-19 detection: leveraging convolutional neural networks in chest x-ray imaging

Mahnoor Jamil; Ikechukwu John Chukwu; Reiner Creutzburg

doi:10.1117/12.3028812

25 June 2024 Improving COVID-19 detection: leveraging convolutional neural networks in chest x-ray imaging

Mahnoor Jamil, Ikechukwu John Chukwu, Reiner Creutzburg

Proceedings Volume 13033, Multimodal Image Exploitation and Learning 2024; 130330S (2024) https://doi.org/10.1117/12.3028812
Event: SPIE Defense + Commercial Sensing, 2024, National Harbor, Maryland, United States

Abstract

The global impact of the COVID-19 pandemic has significantly disrupted healthcare systems worldwide. Amidst challenges, there is a crucial demand for efficient methodologies to expedite disease detection. This research underscores the potential of Deep Neural Networks in enhancing pandemic management over the past five years. Focusing on Artificial Intelligence (AI) application in COVID-19 detection through X-ray imaging, this research advocates using Visual Geometry Group (VGG’16), a Convolutional Neural Network (CNN) used for image classification with multiple layers. These CNNs act as classifier-based systems, treating images as structured data arrays to identify and learn patterns. Quantifying the model’s effectiveness through the accuracy score, this research reveals a 0.90% accuracy, indicating the model’s accurate detection of COVID-19 cases in X-ray images. Additionally, the study highlights a significant achievement with a less than 10% false positive rate, crucial for reliable and prompt COVID-19 diagnoses in the healthcare industry. In conclusion, this research presents an AI-driven approach, utilizing VGG’16 and convolutional neural networks to enhance the efficiency and accuracy of COVID-19 detection in X-ray imaging. The high accuracy score and low false positive rate positions this methodology as a valuable contribution, offering robust pandemic management and healthcare decision-making.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Mahnoor Jamil, Ikechukwu John Chukwu, and Reiner Creutzburg "Improving COVID-19 detection: leveraging convolutional neural networks in chest x-ray imaging", Proc. SPIE 13033, Multimodal Image Exploitation and Learning 2024, 130330S (25 June 2024); https://doi.org/10.1117/12.3028812

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KEYWORDS

COVID 19

Chest imaging

Convolutional neural networks

Data modeling

Medical imaging

Education and training

Performance modeling

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