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Related Experiment Video

Updated: Jul 2, 2026

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

Fast and Accurate Detection of COVID-19 Along With 14 Other Chest Pathologies Using a Multi-Level Classification:

Saleh Albahli^1,2, Ghulam Nabi Ahmad Hassan Yar³

¹Department of Information Technology, College of Computer, Qassim University, Buraydah, Saudi Arabia.

Journal of Medical Internet Research

|February 2, 2021

Summary

This summary is machine-generated.

This study introduces a deep learning pipeline for accurate COVID-19 detection from X-rays, classifying it alongside 14 other chest diseases. The multilevel approach enhances diagnostic speed and accuracy for better healthcare support.

Keywords:

COVID-19 automatic detection biomedical imaging chest x-ray convolutional neural network data augmentation

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Area of Science:

Medical Imaging
Artificial Intelligence
Computer Science

Background:

The rapid spread of COVID-19 necessitates advanced diagnostic tools to support healthcare systems.
Existing deep learning models for chest disease detection often lack the ability to identify multiple conditions simultaneously.

Purpose of the Study:

To develop a fast and accurate diagnostic system for COVID-19 detection using X-ray images.
To classify COVID-19 X-rays against normal cases and 14 other distinct chest diseases.

Main Methods:

A novel, multilevel deep learning pipeline was designed for X-ray image classification.
Transfer learning using ImageNet-pretrained models (ResNet50) facilitated rapid training.
Image segmentation identified lungs and heart, feeding into a two-stage classification process.

Main Results:

The pipeline achieved competitive accuracy for COVID-19 detection alongside 14 other chest diseases.
The two-level classification achieved 96.04% training and 92.52% test accuracy for 3 classes (normal, COVID-19, other).
The second level achieved 88.52% training and 66.63% test accuracy for 14 diseases, outperforming single-stage classification.

Conclusions:

The proposed multilevel pipeline effectively detects COVID-19 and other chest diseases from X-rays with high accuracy.
Dividing the classification task into sequential steps improves diagnostic performance compared to a single-stage approach.