Coronavirus disease 2019, commonly known as COVID-19, is an extremely contagious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2).Computerised Tomography (CT) scans based diagnosis and progression analysis of COVID-19 have recently received academic interest.Most algorithms include two-stage analysis where a slice-level analysis is followed by the patient-level analysis.
However, such an analysis requires labels for individual slices in the training data.In this paper, we propose a single-stage 3D approach that does not require slice-wise labels.Our proposed Dryer Vent Extension method comprises volumetric data pre-processing and 3D ResNet transfer learning.
The pre-processing includes pulmonary segmentation to identify the regions of interest, volume ROSE PETAL resampling and a novel approach for extracting salient slices.This is followed by proposing a region-of-interest aware 3D ResNet for feature learning.The backbone networks utilised in this study include 3D ResNet-18, 3D ResNet-50 and 3D ResNet-101.
Our proposed method employing 3D ResNet-101 has outperformed the existing methods by yielding an overall accuracy of 90%.The sensitivity for correctly predicting COVID-19, Community Acquired Pneumonia (CAP) and Normal class labels in the dataset is 88.2%, 96.
4% and 96.1%, respectively.