A. S. Al-Waisy1, Mazin Abed Mohammed1, Shumoos Al-Fahdawi1, M. S. Maashi2, Begonya Garcia-Zapirain3, Karrar Hameed Abdulkareem4, S. A. Mostafa5, Nallapaneni Manoj Kumar6, Dac-Nhuong Le7,8,*
CMC-Computers, Materials & Continua, Vol.67, No.2, pp. 2409-2429, 2021, DOI:10.32604/cmc.2021.012955
Abstract Coronavirus (COVID-19) epidemic outbreak has devastating effects on daily lives and healthcare systems worldwide. This newly recognized virus is highly transmissible, and no clinically approved vaccine or antiviral medicine is currently available. Early diagnosis of infected patients through effective screening is needed to control the rapid spread of this virus. Chest radiography imaging is an effective diagnosis tool for COVID-19 virus and follow-up. Here, a novel hybrid multimodal deep learning system for identifying COVID-19 virus in chest X-ray (CX-R) images is developed and termed as the COVID-DeepNet system to aid expert radiologists in rapid and accurate image interpretation. First, Contrast-Limited… More >
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