IKONOS: an intelligent tool to support diagnosis of COVID-19 by texture analysis of X-ray images

Abstract

Purpose: In late 2019, the SARS-CoV-2 virus spread worldwide. The virus has high rates of proliferation and causes severe respiratory symptoms, such as pneumonia. The standard diagnostic method for pneumonia is chest X-ray image. There are many advantages to using COVID-19 diagnostic X-rays: low cost, fast, and widely available. Methods: We propose an intelligent system to support diagnosis by X-ray images. We tested Haralick and Zernike moments for feature extraction. Experiments with classic classifiers were done. Results: Support vector machines stood out, reaching an average accuracy of 89.78%, average sensitivity of 0.8979, and average precision and specificity of 0.8985 and 0.9963, respectively. Conclusion: Using features based on textures and shapes combined with classical classifiers, the developed system was able to differentiate COVID-19 from viral and bacterial pneumonia with low computational cost.