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Updated: Oct 6, 2025

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

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

Published on: December 19, 2020

Bayesian-based optimized deep learning model to detect COVID-19 patients using chest X-ray image data.

Mohamed Loey¹, Shaker El-Sappagh², Seyedali Mirjalili³

¹Department of Computer Science, Faculty of Computers and Artificial Intelligence, Benha University, Benha, 13518, Egypt; Information Technology Program, New Cairo Technological University, New Cairo, Egypt; Computer Engineering Department, Cybersecurity Department, Engineering and Information Technology College, Buraydah Colleges, Buraydah, Al-Qassim, Saudi Arabia.

Computers in Biology and Medicine

|January 13, 2022

Summary

This summary is machine-generated.

A new Bayesian optimization-based Convolutional Neural Network (CNN) model accurately identifies Coronavirus Disease 2019 (COVID-19) from chest X-rays. This deep learning approach achieved 96% accuracy, offering a reliable tool for rapid COVID-19 diagnosis.

Keywords:

Bayesian optimization COVID-19 Convolutional neural network Deep learning Image classification Optimization

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Published on: November 30, 2022

Area of Science:

Artificial Intelligence
Medical Imaging
Deep Learning

Background:

Coronavirus Disease 2019 (COVID-19) is a highly contagious global pandemic.
Accurate and rapid patient identification is crucial for disease control.
Deep learning shows significant potential in medical image analysis.

Purpose of the Study:

To develop and evaluate a novel deep learning model for classifying COVID-19 chest X-ray images.
To improve the accuracy of COVID-19 detection in diverse real-world scenarios.

Main Methods:

A Convolutional Neural Network (CNN) model was designed to extract deep features from chest X-ray images.
Bayesian optimization was employed to fine-tune CNN hyperparameters for optimal performance.
A large dataset of 10,848 chest X-ray images (COVID-19, normal, and pneumonia) was utilized.

Main Results:

The proposed Bayesian optimization-based CNN model achieved 96% accuracy in classifying COVID-19 chest X-ray images.
Ablation studies confirmed the effectiveness of the Bayesian optimization approach compared to other scenarios.
The model demonstrated high trustworthiness and accuracy in real-world application.

Conclusions:

The novel Bayesian optimization-CNN model offers a highly accurate and reliable method for COVID-19 detection using chest X-rays.
This deep learning approach can significantly aid in the rapid and precise identification of COVID-19 patients.
The model's performance suggests its potential for integration into clinical diagnostic workflows.