IKONOS: An intelligent tool to support diagnosis of Covid-19 by texture analysis of x-ray images

Abstract

In late 2019, the SARS-Cov-2 spread worldwide. The virus has high rates of proliferation and causes severe respiratory symptoms, such as pneumonia. There is still no specific treatment and diagnosis for the disease. 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. 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. Support vector machines stood out, reaching an average accuracy of 89.78%, average recall and sensitivity of 0.8979, and average precision and specificity of 0.8985 and 0.9963 respectively. The system is able to differentiate Covid-19 from viral and bacterial pneumonia, with low computational cost.

Applications of artificial intelligence in battling against covid-19: A literature review

“…Other examples of approaches that use machine learning techniques for processing CT scan and X-Ray images can be found in [353] , [354] , [355] , [356] , [357] , [358] , [359] , [360] , [361] , [362] , [363] , [364] , [365] , [366] , [367] , [368] , [369] , [370] , [371] .…”

AIoT Used for COVID-19 Pandemic Prevention and Control

“…With the help of the supervised learning method based on statistical learning theory (SVM algorithm), features can be also directly extracted to determine whether the disease is present [32]. e sensitivity of their experiment is higher than that of the study in [26] and thus makes it easier for doctors to reduce the rate of missed tests.…”

“…Contrast-constrained adaptive histogram equalization and convolutional neural network are used to analyze the data sets. Gomes et al [ 26 ] propose an intelligent system to support X-ray scan image diagnosis and develop IKONOS (a desktop application) to diagnose COVID-19 via X-ray image, as shown in Figure 6 . After receiving an image, the doctor uploads it to the app, which uses texture and shape descriptors or classical classifiers for feature extraction and makes analysis by the intelligent system to identify COVID-19.…”

Machine Learning Approaches for Temporal and Spatio-Temporal Covid-19 Forecasting: A Brief Review and a Contribution

“…They used LASSO to find the 12 most discriminating characteristics in the distinction between Covid-19 and other pneumonias. Gomes et al [28] proposed a system to support the diagnosis of Covid-19 by analyzing chest X-ray images, capable of differentiating Covid-19 from bacterial and viral pneumonias using texture-based image representation and classification by Random Forests.…”

“…Different from other more complex Covid-19 X-ray feature extraction approaches [7,8,12,19,33,35,45,53,54,63,66,96], Gomes et al [28] avoided deep learning based solutions and adopted texture and shape features to provide the users a lowcost computational web-based computational environment able to deal with several simultaneous users without overcharging network resources.…”