Covid-19 diagnosis by WE-SAJ

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 leaming 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 feature mutual information 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.

Automated summary

With the global COVID-19 pandemic, the fast diagnosis and monitoring of the disease have become crucial challenges. This research presents a deep learning model called WE-SAJ, which utilizes artificial intelligence techniques to classify CT images and distinguish infected patients from healthy populations. The experiments show the superior performance of the model and the effectiveness of the Self-adaptive Jaya algorithm in medical image classification tasks.