Covid-19 Diagnostic Using 3d Deep Transfer Learning for Classification of Volumetric Computerised Tomography Chest Scans
Shuohan Xue, Charith Abhayaratne
Abstract
Deep learning-based algorithms provide an efficient and reliable diagnosis for medical imaging. This paper proposes COVID-19 diagnosis based on analysis of Computerised tomography (CT) chest scans. In recent years, deep learning-based analysis of CT chest scans has demonstrated competitive sensitivity for pneumonia prognosis. This paper presents our submission for the 2021 ICASSP Signal Processing Grand Challenge (SPGC). We exploit a 3D Network-based transfer learning approach to classify volumetric CT scans with a novel pre-processing method to render the volume with salient features. This work uses the pre-trained 3D ResNet50 as the backbone network. The 3D network is trained on a dataset consisting of 3 classes: Community Acquired Pneumonia (CAP), COVID-19 and Normal patient. The final testing results have shown an overall accuracy of 85.56% with the COVID-19 sensitivity attaining 82.86%.