Automatic image processing to identify post-COVID conditions by using deep learning (7 pages)

• A. Hernández-Trinidad ^[1] ; T. Córdova-Fraga ^[1] ; L.C. Padierna-García ^[1] ; J.L. López-Hernández ^[2] ; B.O. Murillo-Ortiz ^[2] ; R. Guzmán-Cabrera ^[1]
  1. [1] Universidad de Guanajuato

     Universidad de Guanajuato

     México

     
  2. [2] Unidad de Investigación en Epidemiología, OOAD Guanajuato, León, México
• Localización: Revista Mexicana de Física, ISSN-e 0035-001X, Vol. 69, Nº. 6, 2023
• Idioma: inglés
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• Resumen

  • In the present research, a supervised learning classification methodology is proposed to identify post-COVID conditions. Image processing and deep learning methods were employed to analyze a data set provided by the High Specialty Medical Unit No.1 of the Mexican Institute of Social Security (T1-IMSS) of Leon, Guanajuato, Mexico, of Mexican patients infected with COVID-19. The dataset is classified into post-COVID findings and no post-COVID findings. A deep neural network of 50 hidden layers is used to extract regions of interest, with properties that can potentially be related to computer-aided medical diagnosis. Different patterns were found in the post-COVID computed tomography scans: pulmonary fibrosis, ground glass pattern, etc. The efficiency of the proposed method was97% precision using the cross-validation classification scenario. This result allows to provide an auxiliary tool in medical diagnosis, through computer-aided diagnosis. This model provides an automatic and objective estimation of post-COVID conditions of Mexican patients, facilitating the expert interpretation during the COVID-19 pandemic.