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  • Open Access


    Deep Learning for COVID-19 Diagnosis via Chest Images

    Shuihua Wang1,2, Yudong Zhang2,*

    CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 129-132, 2023, DOI:10.32604/cmc.2023.040560

    Abstract This article has no abstract. More >

  • Open Access


    Quantum Inspired Differential Evolution with Explainable Artificial Intelligence-Based COVID-19 Detection

    Abdullah M. Basahel, Mohammad Yamin*

    Computer Systems Science and Engineering, Vol.46, No.1, pp. 209-224, 2023, DOI:10.32604/csse.2023.034449

    Abstract Recent advancements in the Internet of Things (Io), 5G networks, and cloud computing (CC) have led to the development of Human-centric IoT (HIoT) applications that transform human physical monitoring based on machine monitoring. The HIoT systems find use in several applications such as smart cities, healthcare, transportation, etc. Besides, the HIoT system and explainable artificial intelligence (XAI) tools can be deployed in the healthcare sector for effective decision-making. The COVID-19 pandemic has become a global health issue that necessitates automated and effective diagnostic tools to detect the disease at the initial stage. This article presents a new quantum-inspired differential evolution… More >

  • Open Access


    PSTCNN: Explainable COVID-19 diagnosis using PSO-guided self-tuning CNN


    BIOCELL, Vol.47, No.2, pp. 373-384, 2023, DOI:10.32604/biocell.2023.025905

    Abstract Since 2019, the coronavirus disease-19 (COVID-19) has been spreading rapidly worldwide, posing an unignorable threat to the global economy and human health. It is a disease caused by severe acute respiratory syndrome coronavirus 2, a single-stranded RNA virus of the genus Betacoronavirus. This virus is highly infectious and relies on its angiotensin-converting enzyme 2-receptor to enter cells. With the increase in the number of confirmed COVID-19 diagnoses, the difficulty of diagnosis due to the lack of global healthcare resources becomes increasingly apparent. Deep learning-based computer-aided diagnosis models with high generalisability can effectively alleviate this pressure. Hyperparameter tuning is essential in… More >

  • Open Access


    Covid-19 Diagnosis Using a Deep Learning Ensemble Model with Chest X-Ray Images

    Fuat Türk*

    Computer Systems Science and Engineering, Vol.45, No.2, pp. 1357-1373, 2023, DOI:10.32604/csse.2023.030772

    Abstract Covid-19 is a deadly virus that is rapidly spread around the world towards the end of the 2020. The consequences of this virus are quite frightening, especially when accompanied by an underlying disease. The novelty of the virus, the constant emergence of different variants and its rapid spread have a negative impact on the control and treatment process. Although the new test kits provide almost certain results, chest X-rays are extremely important to detect the progression and degree of the disease. In addition to the Covid-19 virus, pneumonia and harmless opacity of the lungs also complicate the diagnosis. Considering the… More >

  • Open Access


    Residual Attention Deep SVDD for COVID-19 Diagnosis Using CT Scans

    Akram Ali Alhadad1,2,*, Omar Tarawneh3, Reham R. Mostafa1, Hazem M. El-Bakry1

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 3333-3350, 2023, DOI:10.32604/cmc.2023.033413

    Abstract COVID-19 is the common name of the disease caused by the novel coronavirus (2019-nCoV) that appeared in Wuhan, China in 2019. Discovering the infected people is the most important factor in the fight against the disease. The gold-standard test to diagnose COVID-19 is polymerase chain reaction (PCR), but it takes 5–6 h and, in the early stages of infection, may produce false-negative results. Examining Computed Tomography (CT) images to diagnose patients infected with COVID-19 has become an urgent necessity. In this study, we propose a residual attention deep support vector data description SVDD (RADSVDD) approach to diagnose COVID-19. It is… More >

  • Open Access


    A Deep Learning Framework for COVID-19 Diagnosis from Computed Tomography

    Nabila Mansouri1,2,*, Khalid Sultan3, Aakash Ahmad4, Ibrahim Alseadoon4, Adal Alkhalil4

    Intelligent Automation & Soft Computing, Vol.34, No.2, pp. 1247-1264, 2022, DOI:10.32604/iasc.2022.025046

    Abstract The outbreak of novel Coronavirus COVID-19, an infectious disease caused by the SARS-CoV-2 virus, has caused an unprecedented medical, economic, and social emergency that requires data-driven intelligence and decision support systems to counter the subsequent pandemic. Data-driven models and intelligent systems can assist medical researchers and practitioners to identify symptoms of COVID-19 infection. Several solutions based on medical image processing have been proposed for this purpose. However, the most shortcoming of hand craft image processing systems is the lower provided performances. Hence, for the first time, the proposed solution uses a deep learning model that is applied to Computed Tomography… More >

  • Open Access


    A Lightweight CNN Based on Transfer Learning for COVID-19 Diagnosis

    Xiaorui Zhang1,2,3,*, Jie Zhou2, Wei Sun3,4, Sunil Kumar Jha5

    CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 1123-1137, 2022, DOI:10.32604/cmc.2022.024589

    Abstract The key to preventing the COVID-19 is to diagnose patients quickly and accurately. Studies have shown that using Convolutional Neural Networks (CNN) to analyze chest Computed Tomography (CT) images is helpful for timely COVID-19 diagnosis. However, personal privacy issues, public chest CT data sets are relatively few, which has limited CNN's application to COVID-19 diagnosis. Also, many CNNs have complex structures and massive parameters. Even if equipped with the dedicated Graphics Processing Unit (GPU) for acceleration, it still takes a long time, which is not conductive to widespread application. To solve above problems, this paper proposes a lightweight CNN classification… More >

  • Open Access


    A Survey on Machine Learning in COVID-19 Diagnosis

    Xing Guo1,#, Yu-Dong Zhang2,#, Siyuan Lu2, Zhihai Lu1,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.130, No.1, pp. 23-71, 2022, DOI:10.32604/cmes.2021.017679

    Abstract Since Corona Virus Disease 2019 outbreak, many expert groups worldwide have studied the problem and proposed many diagnostic methods. This paper focuses on the research of Corona Virus Disease 2019 diagnosis. First, the procedure of the diagnosis based on machine learning is introduced in detail, which includes medical data collection, image preprocessing, feature extraction, and image classification. Then, we review seven methods in detail: transfer learning, ensemble learning, unsupervised learning and semi-supervised learning, convolutional neural networks, graph neural networks, explainable deep neural networks, and so on. What’s more, the advantages and limitations of different diagnosis methods are compared. Although the… More >

  • Open Access


    Classification Framework for COVID-19 Diagnosis Based on Deep CNN Models

    Walid El-Shafai1, Abeer D. Algarni2,*, Ghada M. El Banby3, Fathi E. Abd El-Samie1,2, Naglaa F. Soliman2,4

    Intelligent Automation & Soft Computing, Vol.31, No.3, pp. 1561-1575, 2022, DOI:10.32604/iasc.2022.020386

    Abstract Automated diagnosis based on medical images is a very promising trend in modern healthcare services. For the task of automated diagnosis, there should be flexibility to deal with an enormous amount of data represented in the form of medical images. In addition, efficient algorithms that could be adapted according to the nature of images should be used. The importance of automated medical diagnosis has been maximized with the evolution of COVID-19 pandemic. COVID-19 first appeared in China, Wuhan, and then it has exploded in the whole world with a very bad impact on our daily life. The third wave of… More >

  • Open Access


    Deep Optimal VGG16 Based COVID-19 Diagnosis Model

    M. Buvana1, K. Muthumayil2, S. Senthil kumar3, Jamel Nebhen4, Sultan S. Alshamrani5, Ihsan Ali6,*

    CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 43-58, 2022, DOI:10.32604/cmc.2022.019331

    Abstract Coronavirus (COVID-19) outbreak was first identified in Wuhan, China in December 2019. It was tagged as a pandemic soon by the WHO being a serious public medical condition worldwide. In spite of the fact that the virus can be diagnosed by qRT-PCR, COVID-19 patients who are affected with pneumonia and other severe complications can only be diagnosed with the help of Chest X-Ray (CXR) and Computed Tomography (CT) images. In this paper, the researchers propose to detect the presence of COVID-19 through images using Best deep learning model with various features. Impressive features like Speeded-Up Robust Features (SURF), Features from… More >

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