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Related Experiment Video

Updated: Oct 16, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

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Published on: November 30, 2022

COVID-19 Diagnosis from CT Images with Convolutional Neural Network Optimized by Marine Predator Optimization

Huaping Jia¹, Junlong Zhao², Ali Arshaghi³

¹College of Computer, Weinan Normal University, Weinan, Shaanxi, China.

Biomed Research International

|October 15, 2021

Summary

This study introduces an advanced AI method for diagnosing Coronavirus Disease 2019 (COVID-19) using CT scans. The novel approach significantly improves diagnostic accuracy and reliability for effective pandemic control.

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Area of Science:

Medical Imaging
Artificial Intelligence
Computational Biology

Background:

The global spread of Coronavirus Disease 2019 (COVID-19) necessitates rapid and accurate diagnostic tools.
Timely diagnosis and isolation of infected individuals are critical for controlling the pandemic.
Current diagnostic methods face challenges in speed and accessibility.

Purpose of the Study:

To develop and evaluate a novel hybrid deep learning model for automated COVID-19 diagnosis from CT images.
To enhance diagnostic performance through optimization using a metaheuristic algorithm.
To compare the proposed method against existing techniques for validation.

Main Methods:

A hybrid convolutional neural network (CNN) architecture was employed for image analysis.
The CNN model was optimized using the marine predator optimization algorithm (MPA).
The method was trained and validated on the MosMedData dataset of chest CT scans.

Main Results:

The proposed hybrid CNN-MPA model achieved high performance metrics.
Achieved accuracy of 98.11%, precision of 98.13%, sensitivity of 98.66%, and F1 score of 97.26%.
Outperformed three other comparative methods in all evaluated indicators.

Conclusions:

The developed AI-based method demonstrates superior accuracy and reliability for COVID-19 diagnosis using CT scans.
This approach offers a promising tool for rapid and effective screening of COVID-19 patients.
The integration of metaheuristic optimization significantly enhances the diagnostic capabilities of deep learning models.