COVID-19 detection in X-ray images using convolutional neural networks

Arias-Garzón, Daniel; Alzate-Grisales, Jesús Alejandro; Orozco-Arias, Simon; Arteaga-Arteaga, Harold Brayan; Bravo-Ortiz, Mario Alejandro; Mora-Rubio, Alejandro; Saborit-Torres, Jose Manuel; Serrano, Joaquim Ángel Montell; Tabares-Soto, Reinel; Vayá, Maria de la Iglesia

COVID-19 global pandemic affects health care and lifestyle worldwide, and its early detection is critical to control cases’ spreading and mortality. The actual leader diagnosis test is the Reverse transcription Polymerase chain reaction (RT-PCR), result times and cost of these tests are high, so other fast and accesible diagnostic tools are needed. Inspired by recent research that correlates the presence of COVID-19 to findings in Chest X-ray images, this papers’ approach uses existing deep learning models (VGG19 and U-Net) to process these images and classify them as positive or negative for COVID-19. The proposed system involves a preprocessing stage with lung segmentation, removing the surroundings which does not offer relevant information for the task and may produce biased results; after this initial stage comes the classification model trained under the transfer learning scheme; and finally, results analysis and interpretation via heat maps visualization. The best models achieved a detection accuracy of COVID-19 around 97%.