What question did this study set out to answer?

To develop and evaluate a deep learning model for the rapid diagnosis of COVID-19 using CT images.

March 6, 2022Systems Science & Control Engineering65 citationsOpen Access

Covid-19 diagnosis by WE-SAJ

Key Points

To develop and evaluate a deep learning model for the rapid diagnosis of COVID-19 using CT images.
Utilized wavelet entropy for feature extraction from CT images.
Implemented two-layer feedforward neural networks (FNNs) for classification.
Applied the adaptive Jaya algorithm as the training algorithm.
Achieved sensitivity of 85.47±1.84 and specificity of 87.23±1.67.
Reported an accuracy of 86.35±0.70 and an F1 score of 86.23±0.77.
Demonstrated better performance than the standard Jaya-based model in medical image classification tasks.

Abstract

With a global COVID-19 pandemic, the number of confirmed patients increases rapidly, leaving the world with very few medical resources. Therefore, the fast diagnosis and monitoring of COVID-19 are one of the world's most critical challenges today. Artificial intelligence-based CT image classification models can quickly and accurately distinguish infected patients from healthy populations. Our research proposes a deep learning model (WE-SAJ) using wavelet entropy for feature extraction, two-layer FNNs for classification and the adaptive Jaya algorithm as a training algorithm. It achieves superior performance compared to the Jaya-based model. The model has a sensitivity of 85.47±1.84, specificity of 87.23±1.67 precision of 87.03±1.34, an accuracy of 86.35±0.70, and F1 score of 86.23±0.77, Matthews correlation coefficient of 72.75±1.38, and Fowlkes-Mallows Index of 86.24±0.76. Our experiments demonstrate the potential of artificial intelligence techniques for COVID-19 diagnosis and the effectiveness of the Self-adaptive Jaya algorithm compared to the Jaya algorithm for medical image classification tasks.

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