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Monkeypox Virus Detection Using Pre-trained Deep Learning-based Approaches.

Chiranjibi Sitaula¹, Tej Bahadur Shahi^2,3

¹Department of Electrical and Computer Systems Engineering, Monash University, Wellignton Rd, Clayton, VIC, 3800, Australia. chiranjibi.sitaula@monash.edu.

Journal of Medical Systems

|October 6, 2022

Summary

This summary is machine-generated.

Early detection of monkeypox virus is crucial. This study shows an ensemble of 13 deep learning models achieved high accuracy (87.13%) for monkeypox detection, outperforming current methods for mass screening.

Keywords:

Classification Deep learning Detection Monkeypox Pandemic SARS-Cov2

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Area of Science:

Virology
Artificial Intelligence
Machine Learning

Background:

Monkeypox virus is emerging globally, raising concerns about potential pandemic spread similar to COVID-19.
Early detection is critical to prevent widespread community transmission of monkeypox.
Artificial Intelligence (AI) offers a promising avenue for early-stage detection of infectious diseases.

Purpose of the Study:

To compare the performance of 13 pre-trained deep learning (DL) models for monkeypox virus detection.
To develop an ensemble approach using the best-performing DL models to enhance detection accuracy.
To evaluate the proposed ensemble method on a publicly available dataset.

Main Methods:

Fine-tuning 13 different pre-trained deep learning models with universal custom layers.
Evaluating model performance using Precision, Recall, F1-score, and Accuracy metrics.
Ensembling the top-performing DL models via majority voting on probabilistic outputs.

Main Results:

The proposed ensemble approach achieved an average Precision of 85.44%, Recall of 85.47%, F1-score of 85.40%, and Accuracy of 87.13%.
The ensemble method demonstrated superior performance compared to existing state-of-the-art methods.
Individual DL models showed varying performance, highlighting the benefit of ensemble techniques.

Conclusions:

The developed AI-based ensemble approach is effective for early and accurate monkeypox virus detection.
The findings suggest the applicability of this method for mass screening by health practitioners.
This study provides a robust framework for utilizing deep learning in combating emerging viral threats.