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Updated: Oct 12, 2025

Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia

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Published on: December 19, 2020

COVID-19 Detection Using Deep Learning Algorithm on Chest X-ray Images.

Shamima Akter¹, F M Javed Mehedi Shamrat², Sovon Chakraborty³

¹Department of Bioinformatics and Computational Biology, George Mason University, Fairfax, VA 22030, USA.

|November 27, 2021

Summary

This summary is machine-generated.

This study introduces a modified MobileNetV2 deep learning model for accurate COVID-19 detection using chest X-rays. The proposed method achieved 98% accuracy, outperforming existing Convolutional Neural Network (CNN) models.

Keywords:

CNN COVID-19 Mobilenetv2 chest X-ray image modified MobileNetV2 performance evaluation

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Area of Science:

Medical Imaging
Artificial Intelligence
Pulmonology

Background:

COVID-19, a severe respiratory illness, necessitates rapid and accurate diagnostic tools.
Chest X-ray imaging is crucial for diagnosing COVID-19 due to its primary impact on the lungs.
Automated screening methods can enhance early detection and minimize healthcare professional exposure.

Purpose of the Study:

To develop and evaluate an automatic deep learning classification method for COVID-19 detection from chest X-ray images.
To compare the performance of eleven existing Convolutional Neural Network (CNN) models for COVID-19 classification.
To propose a modified MobileNetV2 model for improved accuracy and efficiency in COVID-19 diagnosis.

Main Methods:

A dataset of 3616 COVID-19 and 10,192 healthy chest X-rays was augmented to 26,000 images each.
Data preprocessing involved histogram equalization, spectrum, grays, cyan, and NCLAHE normalization.
Eleven CNN models (VGG16, VGG19, MobileNetV2, InceptionV3, NFNet, ResNet50, ResNet101, DenseNet, EfficientNetB7, AlexNet, GoogLeNet) were initially employed, followed by modification of MobileNetV2.

Main Results:

The modified MobileNetV2 model achieved the highest accuracy of 98% in classifying COVID-19 from chest X-rays.
Pre-trained MobileNetV2, VGG19, and ResNet101 models showed accuracies of 97%, 95%, and 95%, respectively.
The proposed model demonstrated the shortest compilation time (2 h, 50 min, 21 s) and statistically significant performance.

Conclusions:

The proposed modified MobileNetV2 deep learning model offers a highly accurate and efficient method for COVID-19 detection using chest X-rays.
This automated approach surpasses existing methods in identifying infection symptoms from radiographic images.
The findings support the clinical utility of AI-driven tools for rapid COVID-19 screening.