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  • Open Access


    Efficient Grad-Cam-Based Model for COVID-19 Classification and Detection

    Saleh Albahli1,*, Ghulam Nabi Ahmad Hassan Yar2,3

    Computer Systems Science and Engineering, Vol.44, No.3, pp. 2743-2757, 2023, DOI:10.32604/csse.2023.024463

    Abstract Corona Virus (COVID-19) is a novel virus that crossed an animal-human barrier and emerged in Wuhan, China. Until now it has affected more than 119 million people. Detection of COVID-19 is a critical task and due to a large number of patients, a shortage of doctors has occurred for its detection. In this paper, a model has been suggested that not only detects the COVID-19 using X-ray and CT-Scan images but also shows the affected areas. Three classes have been defined; COVID-19, normal, and Pneumonia for X-ray images. For CT-Scan images, 2 classes have been defined COVID-19 and non-COVID-19. For… More >

  • Open Access


    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 to several state-of-the-art methods. In… More >

  • Open Access


    Combining CNN and Grad-Cam for COVID-19 Disease Prediction and Visual Explanation

    Hicham Moujahid1, Bouchaib Cherradi1,2,*, Mohammed Al-Sarem3, Lhoussain Bahatti1, Abou Bakr Assedik Mohammed Yahya Eljialy4, Abdullah Alsaeedi3, Faisal Saeed3

    Intelligent Automation & Soft Computing, Vol.32, No.2, pp. 723-745, 2022, DOI:10.32604/iasc.2022.022179

    Abstract With daily increasing of suspected COVID-19 cases, the likelihood of the virus mutation increases also causing the appearance of virulent variants having a high level of replication. Automatic diagnosis methods of COVID-19 disease are very important in the medical community. An automatic diagnosis could be performed using machine and deep learning techniques to analyze and classify different lung X-ray images. Many research studies proposed automatic methods for detecting and predicting COVID-19 patients based on their clinical data. In the leak of valid X-ray images for patients with COVID-19 datasets, several researchers proposed to use augmentation techniques to bypass this limitation.… More >

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