One Shot Model for COVID-19 Classification and Lesions Segmentation in Chest CT Scans Using Long Short-Term Memory Network With Attention Mechanism

In this article, we present a model that fuses lesion segmentation with attention mechanism to predict coronavirus (COVID-19) from chest CT scans. The model segments lesions, extracts regions of interest from scans and applies attention to them to determine the most relevant ones for image classification. Additionally, we augment the model with long-short term memory network layers that learn features from a sequence of regions of interest before computing attention. The model is trained in one shot for both problems, using two different sets of data. We achieve 0.4683 mean average precision for lesion segmentation, 95.74% COVID-19 sensitivity and 98.15% class-adjusted F1 score for image classification on a large CNCB-NCOV dataset.