Open Access

ARTICLE

X-Ray Covid-19 Detection Based on Scatter Wavelet Transform and Dense Deep Neural Network

Ali Sami Al-Itbi^*, Ahmed Bahaaulddin A. Alwahhab, Ali Mohammed Sahan

Informatics Department, Technical College of Management, Middle Technical University, Baghdad, Iraq

* Corresponding Author: Ali Sami Al-Itbi. Email:

Computer Systems Science and Engineering 2022, 41(3), 1255-1271. https://doi.org/10.32604/csse.2022.021980

Received 23 July 2021; Accepted 26 August 2021; Issue published 10 November 2021

Abstract

Notwithstanding the discovery of vaccines for Covid-19, the virus's rapid spread continues due to the limited availability of vaccines, especially in poor and emerging countries. Therefore, the key issues in the present COVID-19 pandemic are the early identification of COVID-19, the cautious separation of infected cases at the lowest cost and curing the disease in the early stages. For that reason, the methodology adopted for this study is imaging tools, particularly computed tomography, which have been critical in diagnosing and treating the disease. A new method for detecting Covid-19 in X-rays and CT images has been presented based on the Scatter Wavelet Transform and Dense Deep Neural Network. The Scatter Wavelet Transform has been employed as a feature extractor, while the Dense Deep Neural Network is utilized as a binary classifier. An extensive experiment was carried out to evaluate the accuracy of the proposed method over three datasets: IEEE 80200, Kaggle, and Covid-19 X-ray image data Sets. The dataset used in the experimental part consists of 14142. The numbers of training and testing images are 8290 and 2810, respectively. The analysis of the result refers that the proposed methods achieved high accuracy of 98%. The proposed model results show an excellent outcome compared to other methods in the same domain, such as (DeTraC) CNN, which achieved only 93.1%, CNN, which achieved 94%, and stacked Multi-Resolution CovXNet, which achieved 97.4%. The accuracy of CapsNet reached 97.24%.

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Citations