Name
Title | ||
|---|---|---|
COVID-19 DIAGNOSTIC USING 3D DEEP TRANSFER LEARNING FOR CLASSIFICATION OF VOLUMETRIC COMPUTERISED TOMOGRAPHY CHEST SCANS |
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%. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/ICASSP39728.2021.9414947 | 2021 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP 2021) |
Keywords | DocType | Citations |
COVID-19, CT scans, Deep Learning, 3D CNN, Transfer Learning, Fully-Automated Classification | Conference | 0 |
PageRank | References | Authors |
0.34 | 0 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shuohan Xue | 1 | 0 | 0.34 |
| Charith Abhayaratne | 2 | 91 | 15.76 |