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DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning

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Deep Learning Based COVID-19 Detection via Hard Voting Ensemble Method.

Asaad Qasim Shareef¹, Sefer Kurnaz¹

¹Department of Electrical Computer Engineering, Altinbas University, Istanbul, Turkey.

Wireless Personal Communications

|June 26, 2023

Summary

This summary is machine-generated.

This study introduces an ensemble deep learning model for COVID-19 detection using X-ray images. The approach achieves high accuracy, aiding early diagnosis and reducing healthcare system strain.

Keywords:

CNN COVID-19 Deep learning Ensemble method Hard voting

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Area of Science:

Medical Imaging
Artificial Intelligence in Healthcare
Infectious Disease Diagnostics

Background:

Global healthcare systems face immense pressure due to the COVID-19 pandemic.
Early and accurate diagnosis is crucial for controlling virus spread and patient treatment.
Medical imaging, particularly X-rays, offers valuable insights into lung conditions.

Purpose of the Study:

To develop and evaluate a novel ensemble approach for COVID-19 identification using X-ray images.
To leverage deep learning and transfer learning for improved diagnostic performance.
To enhance the efficiency of COVID-19 diagnosis in resource-constrained settings.

Main Methods:

An ensemble method combining confidence scores from CNN, VGG16, and DenseNet models using hard voting.
Application of transfer learning to optimize performance on limited medical image datasets.
Utilizing X-ray-PIC (X-ray Pictures) for COVID-19 detection.

Main Results:

The proposed ensemble approach achieved 97% accuracy, 96% precision, 100% recall, and 98% F1-score.
Demonstrated superior performance compared to existing diagnostic techniques.
Validated the effectiveness of transfer learning in enhancing model performance.

Conclusions:

Ensemble methods combined with transfer learning show significant promise for COVID-19 diagnosis via X-ray.
The X-ray-PIC approach can substantially aid in early disease detection.
This methodology has the potential to alleviate the burden on global healthcare systems.