The COVID-19 pandemic has produced social and economic changes that are still affecting our lives. The coronavirus is proinflammatory, it is replicating, and it is quickly spreading. The most affected organ is the lung, and the evolution of the disease can degenerate very rapidly from the early phase, also known as mild to moderate and even severe stages, where the percentage of recovered patients is very low. Therefore, a fast and automatic method to detect the disease stages for patients who underwent a computer tomography investigation can improve the clinical protocol. Transfer learning is used do tackle this issue, mainly by decreasing the computational time. The dataset is composed of images from public databases from 118 patients and new data from 55 patients collected during the COVID-19 spread in Romania in the spring of 2020. Even if the disease detection by the computerized tomography scans was studied using deep learning algorithms, to our knowledge, there are no studies related to the multiclass classification of the images into pulmonary damage stages. This could be helpful for physicians to automatically establish the disease severity and decide on the proper treatment for patients and any special surveillance, if needed. An evaluation study was completed by considering six different pre-trained CNNs. The results are encouraging, assuring an accuracy of around 87%. The clinical impact is still huge, even if the disease spread and severity are currently diminished.
Keywords: convolutional neural network; deep learning; medical imaging processing; transfer learning.