A Pipeline for the Diagnosis and Classification of Lung Lesions for Patients with COVID-19

The current study considers the development of a 5-layer pipeline for identifying and classifying COVID-19-induced lung lesions. Such system is multilayer, built upon convolutional and fully connected neural networks and logistic self-organised forest built using the group method of data handling (GMDH) principles. This pipeline includes a mechanism for finding lesions regions in lungs computer tomography images and for calculating related lung damage volume. The layer for finding images with lesions reached a Matthews Correlation Coefficient of 0.98. The layer for lesions segmentation reached a Dice similarity coefficient of 0.74, while the layer for lesions classification reached Fl-scores of 1, 0.95, 0.93 respectively for the ground-glass, opacity, crazy-paving and consolidation lesion type. Results demonstrate the effectiveness of the implemented multi-layer system in solving tasks of lesions identification and classification while being composed into a single pipeline.