Predicting the Severity of COVID-19 Pneumonia from Chest X-Ray Images: A Convolutional Neural Network Approach

Thien B. Nguyen-Tat; Viet-Trinh Tran-Thi; Vuong M. Ngo

doi:10.4108/eetinis.v12i1.6240

Authors

Thien B. Nguyen-Tat University Of Information Technology https://orcid.org/0000-0002-4809-7126
Viet-Trinh Tran-Thi University Of Information Technology
Vuong M. Ngo Ho Chi Minh City Open University

DOI:

https://doi.org/10.4108/eetinis.v12i1.6240

Keywords:

Pneumonia Severity, COVID-19, Convolutional Neural Networks, Classification

Abstract

This study addresses significant limitations of previous works based on the Brixia and COVIDGR datasets, which primarily provided qualitative lung injury scores and focused mainly on detecting mild and moderate cases. To bridge these critical gaps, we developed a unified and comprehensive analytical framework that accurately assesses COVID-19-induced lung injuries across four levels: Normal, Mild, Moderate, and Severe. This approach’s core is a meticulously curated, balanced dataset comprising 9,294 high-quality chest X-ray images. Notably, this dataset has been made widely available to the research community, fostering collaborative efforts and enhancing the precision of lung injury classification at all severity levels. To validate the framework’s effectiveness, we conducted an in-depth evaluation using advanced deep learning models, including VGG16, RegNet, DenseNet, MobileNet, EfficientNet, and Vision Transformer (ViT), on this dataset. The top-performing model was further enhanced by optimizing additional fully connected layers and adjusting weights, achieving an outstanding sensitivity of 94.38%. These results affirm the accuracy and reliability of the proposed solution and demonstrate its potential for broad application in clinical practice. Our study represents a significant step forward in developing AI-powered diagnostic tools, contributing to the timely and precise diagnosis of COVID-19 cases. Furthermore, our dataset and methodological framework hold the potential to serve as a foundation for future research, paving the way for advancements in the detection and classification of respiratory diseases with higher accuracy and efficiency.

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