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  • Open Access

    ARTICLE

    A Transfer Learning Based Approach for COVID-19 Detection Using Inception-v4 Model

    Ali Alqahtani1, Shumaila Akram2, Muhammad Ramzan2,3,*, Fouzia Nawaz2, Hikmat Ullah Khan4, Essa Alhashlan5, Samar M. Alqhtani1, Areeba Waris6, Zain Ali7

    Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 1721-1736, 2023, DOI:10.32604/iasc.2023.025597

    Abstract Coronavirus (COVID-19 or SARS-CoV-2) is a novel viral infection that started in December 2019 and has erupted rapidly in more than 150 countries. The rapid spread of COVID-19 has caused a global health emergency and resulted in governments imposing lock-downs to stop its transmission. There is a significant increase in the number of patients infected, resulting in a lack of test resources and kits in most countries. To overcome this panicked state of affairs, researchers are looking forward to some effective solutions to overcome this situation: one of the most common and effective methods is… More >

  • Open Access

    ARTICLE

    Practical Machine Learning Techniques for COVID-19 Detection Using Chest X-Ray Images

    Yurananatul Mangalmurti, Naruemon Wattanapongsakorn*

    Intelligent Automation & Soft Computing, Vol.34, No.2, pp. 733-752, 2022, DOI:10.32604/iasc.2022.025073

    Abstract This paper presents effective techniques for automatic detection/classification of COVID-19 and other lung diseases using machine learning, including deep learning with convolutional neural networks (CNN) and classical machine learning techniques. We had access to a large number of chest X-ray images to use as input data. The data contains various categories including COVID-19, Pneumonia, Pneumothorax, Atelectasis, and Normal (without disease). In addition, chest X-ray images with many findings (abnormalities and diseases) from the National Institutes of Health (NIH) was also considered. Our deep learning approach used a CNN architecture with VGG16 and VGG19 models which… More >

  • Open Access

    ARTICLE

    Multi-Modality and Feature Fusion-Based COVID-19 Detection Through Long Short-Term Memory

    Noureen Fatima1, Rashid Jahangir2, Ghulam Mujtaba1, Adnan Akhunzada3,*, Zahid Hussain Shaikh4, Faiza Qureshi1

    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 4357-4374, 2022, DOI:10.32604/cmc.2022.023830

    Abstract The Coronavirus Disease 2019 (COVID-19) pandemic poses the worldwide challenges surpassing the boundaries of country, religion, race, and economy. The current benchmark method for the detection of COVID-19 is the reverse transcription polymerase chain reaction (RT-PCR) testing. Nevertheless, this testing method is accurate enough for the diagnosis of COVID-19. However, it is time-consuming, expensive, expert-dependent, and violates social distancing. In this paper, this research proposed an effective multi-modality-based and feature fusion-based (MMFF) COVID-19 detection technique through deep neural networks. In multi-modality, we have utilized the cough samples, breathe samples and sound samples of healthy as… More >

  • Open Access

    ARTICLE

    COVID-19 Detection via a 6-Layer Deep Convolutional Neural Network

    Shouming Hou, Ji Han*

    CMES-Computer Modeling in Engineering & Sciences, Vol.130, No.2, pp. 855-869, 2022, DOI:10.32604/cmes.2022.016621

    Abstract Many people around the world have lost their lives due to COVID-19. The symptoms of most COVID-19 patients are fever, tiredness and dry cough, and the disease can easily spread to those around them. If the infected people can be detected early, this will help local authorities control the speed of the virus, and the infected can also be treated in time. We proposed a six-layer convolutional neural network combined with max pooling, batch normalization and Adam algorithm to improve the detection effect of COVID-19 patients. In the 10-fold cross-validation methods, our method is superior More >

  • Open Access

    ARTICLE

    An Improved DeepNN with Feature Ranking for Covid-19 Detection

    Noha E. El-Attar1,*, Sahar F. Sabbeh1,2, Heba Fasihuddin2, Wael A. Awad3

    CMC-Computers, Materials & Continua, Vol.71, No.2, pp. 2249-2269, 2022, DOI:10.32604/cmc.2022.022673

    Abstract The outbreak of Covid-19 has taken the lives of many patients so far. The symptoms of COVID-19 include muscle pains, loss of taste and smell, coughs, fever, and sore throat, which can lead to severe cases of breathing difficulties, organ failure, and death. Thus, the early detection of the virus is very crucial. COVID-19 can be detected using clinical tests, making us need to know the most important symptoms/features that can enhance the decision process. In this work, we propose a modified multilayer perceptron (MLP) with feature selection (MLPFS) to predict the positive COVID-19 cases… More >

  • Open Access

    ARTICLE

    Deep Convolutional Neural Network Approach for COVID-19 Detection

    Yu Xue1,2,*, Bernard-Marie Onzo1, Romany F. Mansour3,4, Shoubao Su4

    Computer Systems Science and Engineering, Vol.42, No.1, pp. 201-211, 2022, DOI:10.32604/csse.2022.022158

    Abstract Coronavirus disease 2019 (Covid-19) is a life-threatening infectious disease caused by a newly discovered strain of the coronaviruses. As by the end of 2020, Covid-19 is still not fully understood, but like other similar viruses, the main mode of transmission or spread is believed to be through droplets from coughs and sneezes of infected persons. The accurate detection of Covid-19 cases poses some questions to scientists and physicians. The two main kinds of tests available for Covid-19 are viral tests, which tells you whether you are currently infected and antibody test, which tells if you… More >

  • 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

    Computer Systems Science and Engineering, Vol.41, No.3, pp. 1255-1271, 2022, DOI:10.32604/csse.2022.021980

    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… More >

  • Open Access

    ARTICLE

    Kernel Granulometric Texture Analysis and Light RES-ASPP-UNET Classification for Covid-19 Detection

    A. Devipriya1, P. Prabu2, K. Venkatachalam3, Ahmed Zohair Ibrahim4,*

    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 651-666, 2022, DOI:10.32604/cmc.2022.020820

    Abstract This research article proposes an automatic frame work for detecting COVID -19 at the early stage using chest X-ray image. It is an undeniable fact that coronovirus is a serious disease but the early detection of the virus present in human bodies can save lives. In recent times, there are so many research solutions that have been presented for early detection, but there is still a lack in need of right and even rich technology for its early detection. The proposed deep learning model analysis the pixels of every image and adjudges the presence of… More >

  • Open Access

    ARTICLE

    An Efficient CNN-Based Hybrid Classification and Segmentation Approach for COVID-19 Detection

    Abeer D. Algarni1,*, Walid El-Shafai2, Ghada M. El Banby3, Fathi E. Abd El-Samie1,2, Naglaa F. Soliman1,4

    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 4393-4410, 2022, DOI:10.32604/cmc.2022.020265

    Abstract COVID-19 remains to proliferate precipitously in the world. It has significantly influenced public health, the world economy, and the persons’ lives. Hence, there is a need to speed up the diagnosis and precautions to deal with COVID-19 patients. With this explosion of this pandemic, there is a need for automated diagnosis tools to help specialists based on medical images. This paper presents a hybrid Convolutional Neural Network (CNN)-based classification and segmentation approach for COVID-19 detection from Computed Tomography (CT) images. The proposed approach is employed to classify and segment the COVID-19, pneumonia, and normal CT… More >

  • Open Access

    ARTICLE

    A Monte Carlo Based COVID-19 Detection Framework for Smart Healthcare

    Tallat Jabeen1,2, Ishrat Jabeen1, Humaira Ashraf2, Nz Jhanjhi3,*, Mamoona Humayun4, Mehedi Masud5, Sultan Aljahdali5

    CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 2365-2380, 2022, DOI:10.32604/cmc.2022.020016

    Abstract COVID-19 is a novel coronavirus disease that has been declared as a global pandemic in 2019. It affects the whole world through person-to-person communication. This virus spreads by the droplets of coughs and sneezing, which are quickly falling over the surface. Therefore, anyone can get easily affected by breathing in the vicinity of the COVID-19 patient. Currently, vaccine for the disease is under clinical investigation in different pharmaceutical companies. Until now, multiple medical companies have delivered health monitoring kits. However, a wireless body area network (WBAN) is a healthcare system that consists of nano sensors… More >

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