Abstract
The global epidemic caused by COVID-19 has had a severe impact on the health of human beings. The virus has wreaked havoc throughout the world since its declaration as a worldwide pandemic and has affected an expanding number of nations in numerous countries around the world. Recently, a substantial amount of work has been done by doctors, scientists, and many others working on the frontlines to battle the effects of the spreading virus. The integration of artificial intelligence, specifically deep- and machine-learning applications, in the health sector has contributed substantially to the fight against COVID-19 by providing a modern innovative approach for detecting, diagnosing, treating, and preventing the virus. In this proposed work, we focus mainly on the role of the speech signal and/or image processing in detecting the presence of COVID-19. Three types of experiments have been conducted, utilizing speech-based, image-based, and speech and image-based models. Long short-term memory (LSTM) has been utilized for the speech classification of the patient’s cough, voice, and breathing, obtaining an accuracy that exceeds 98%. Moreover, CNN models VGG16, VGG19, Densnet201, ResNet50, Inceptionv3, InceptionResNetV2, and Xception have been benchmarked for the classification of chest X-ray images. The VGG16 model outperforms all other CNN models, achieving an accuracy of 85.25% without fine-tuning and 89.64% after performing fine-tuning techniques. Furthermore, the speech–image-based model has been evaluated using the same seven models, attaining an accuracy of 82.22% by the InceptionResNetV2 model. Accordingly, it is inessential for the combined speech–image-based model to be employed for diagnosis purposes since the speech-based and image-based models have each shown higher terms of accuracy than the combined model.
Highlights
Introduction and Literature ReviewSince the outbreak of COVID-19 in December 2019 and its declaration as a global worldwide epidemic, in March 2020, by the World Health Organization (WHO), almost every human being’s life has been threatened by this virus
The image dataset consists of 13,808 chest X-ray (CXR) images that are comprised of 10,192 X-ray images categorized as “healthy” that are collected from RSNA [42] and Kaggle [43]
A confusion matrix is utilized for the long short-term memory (LSTM) performance based on the four classes: true positive, false positive, true negative, and false negative; the performance metric is obtained upon the calculation of these four classification classes
Summary
Since the outbreak of COVID-19 in December 2019 and its declaration as a global worldwide epidemic, in March 2020, by the World Health Organization (WHO), almost every human being’s life has been threatened by this virus. Wang et al [19] introduced COVIDNetCT, a deep CNN architecture dedicated to the detection of COVID-19 cases from chest CT images through a machine-driven design exploration technique They have introduced COVIDx-CT, a benchmark CT image dataset comprised of CT imaging data gathered by the China National Center for bio information, encompassing 104,009 images collected from 1489 patient cases. Their method was evaluated with the models ResNet-50, NASNet-A-Mobile, EfficientNet-B0, and COVIDNet-CT, achieving an accuracy of 98.7%, 98.6%, 98.3%, and 99.1%, respectively.
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