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Hybrid Deep-Learning and Machine-Learning Models for Predicting COVID-19.

Talal S Qaid^1,2, Hussein Mazaar³, Mohammad Yahya H Al-Shamri^4,5

¹Computer Science Department, College of Computer Science, King Khalid University, Abha 61421, Saudi Arabia.

Computational Intelligence and Neuroscience

|August 16, 2021

Summary

This summary is machine-generated.

This study developed artificial intelligence models using deep learning and transfer learning to detect COVID-19 from chest X-rays. The AI models accurately differentiate COVID-19 from other pneumonias, aiding early detection and healthcare system recovery.

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Area of Science:

Medical Imaging
Artificial Intelligence
Deep Learning

Background:

The COVID-19 pandemic severely impacted global health and healthcare systems.
Early detection of COVID-19 is crucial for controlling its spread and mitigating risks.
Distinguishing COVID-19 from other viral pneumonias using chest X-rays is challenging due to overlapping features.

Purpose of the Study:

To develop accurate, general, and robust artificial intelligence models for detecting COVID-19.
To utilize deep learning and transfer learning techniques for improved COVID-19 diagnosis.
To assess the models' ability to discriminate COVID-19 from normal cases and other viral pneumonias.

Main Methods:

Employed deep learning, transfer learning, and hybrid machine learning techniques.
Utilized convolutional neural networks (CNNs) and transfer learning models.
Experimented with two datasets: COVID-19 Radiography Database and a local dataset from Asir Hospital.

Main Results:

Proposed models achieved promising results in detecting COVID-19.
Models successfully discriminated COVID-19 from normal and other viral pneumonia cases.
Hybrid models achieved 100% accuracy for binary classification and 97.8% for multiclass classification.

Conclusions:

AI models, particularly hybrid approaches, show high accuracy in detecting COVID-19 from chest X-rays.
These AI tools can assist clinicians in diagnosing COVID-19 and managing the pandemic.
The developed models offer a potential solution for early and accurate COVID-19 detection.