MIA-3DCNN: COVID-19 Detection Based on a 3D CNN

The first transmissions of a new coronavirus, SARS-CoV-2 (Severe Acute Respiratory Syndrome Coronavirus 2) (Cascella et al.; 2022), occurred at the end of 2019, being identified in the region of Wuhan, China, causing the pandemic of COVID-19 during the following years. The symptoms of COVID-19 can range from none to severe. Among the aggravations of the disease is the severe pneumonia that the infection can cause, potentially allowing its detection through lung images of the infected individual. The 3rd COVID-19 Competition (Kollias et al.; 2023, 2022, 2021)) is an annual challenge that encourages research in the analysis of medical lung images for the detection of COVID-19. This competition uses the COV19-CT-DB database (Arsenos et al.; 2022), containing CT scans of patients with and without COVID-19, collected between September of 2020 and November of 2021. Each computed tomography present in this database is a three-dimensional image, represented by slices, and the number of slices per tomography varies between 50 and 700, according to specifications given at the time of performing the image exam. The annotation of each slice was performed by four professionals, radiologists and pulmonologists, with great experience in the area, with 98% agreement between the specialists during the annotation of the classes. The dataset was then separated into training, validation and test sets, with only the first two available to participants to be used during network training, and the last one for participants to perform inference and evaluate their methods. The competition consists of two challenges: 1. COVID Detection: Challenge that aims to classify lungs between COVID and non-COVID classes.