2020/07/01 by Lucas Pedrosa Soares, Soares, Lucas P., Cesar P. Soares +1
Computer Science · Medicine · #AI in cancer detection #Artificial Intelligence in Healthcare and Education #COVID-19 diagnosis using AI #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #FOS: Electrical engineering #Image and Video Processing (eess.IV) #Machine Learning (cs.LG) #Radiomics and Machine Learning in Medical Imaging #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2007.05494
openalex publication_date 2020/07/01 · openalex created_date 2022/07/26 · openalex updated_date 2026/07/28
In recent months the world has been surprised by the rapid advance of\nCOVID-19. In order to face this disease and minimize its socio-economic\nimpacts, in addition to surveillance and treatment, diagnosis is a crucial\nprocedure. However, the realization of this is hampered by the delay and the\nlimited access to laboratory tests, demanding new strategies to carry out case\ntriage. In this scenario, deep learning models are being proposed as a possible\noption to assist the diagnostic process based on chest X-ray and computed\ntomography images. Therefore, this research aims to automate the process of\ndetecting COVID-19 cases from chest images, using convolutional neural networks\n(CNN) through deep learning techniques. The results can contribute to expand\naccess to other forms of detection of COVID-19 and to speed up the process of\nidentifying this disease. All databases used, the codes built, and the results\nobtained from the models' training are available for open access. This action\nfacilitates the involvement of other researchers in enhancing these models\nsince this can contribute to the improvement of results and, consequently, the\nprogress in confronting COVID-19.\n