| Paper ID | CHLG-1.6 |
| Paper Title |
DETECTING COVID-19 AND COMMUNITY ACQUIRED PNEUMONIA USING CHEST CT SCAN IMAGES WITH DEEP LEARNING |
| Authors |
Shubham Chaudhary, Sadbhawna Thakur, Vinit Jakhetiya, Badri N Subudhi, IIT Jammu, India; Ujjwal Baid, Sharath Chandra Guntuku, University of Pennsylvania, United States |
| Session | CHLG-1: COVID-19 Diagnosis |
| Location | Zoom |
| Session Time: | Monday, 07 June, 09:30 - 12:00 |
| Presentation Time: | Monday, 07 June, 09:30 - 12:00 |
| Presentation |
Poster
|
| Topic |
Grand Challenge: COVID-19 Diagnosis |
| IEEE Xplore Open Preview |
Click here to view in IEEE Xplore |
| Virtual Presentation |
Click here to watch in the Virtual Conference |
| Abstract |
We propose a two-stage Convolutional Neural Network (CNN) based classification framework for detecting COVID-19 and Community Acquired Pneumonia (CAP) using the chest Computed Tomography (CT) scan images. In the first stage, an infection - COVID-19 or CAP, is detected using a pre-trained DenseNet architecture. Then, in the second stage, a fine-grained three-way classification is done using EfficientNet architecture. The proposed COVID+CAP-CNN framework achieved a slice-level classification accuracy of over 94\% at identifying COVID-19 and CAP. Further, the proposed framework has the potential to be an initial screening tool for differential diagnosis of COVID-19 and CAP, achieving a validation accuracy of over 89.3\% at the finer three-way COVID-19, CAP, and healthy classification. Within the IEEE ICASSP 2021 Signal Processing Grand Challenge (SPGC) on COVID-19 Diagnosis, our proposed two-stage classification framework achieved an overall accuracy of 90\% and sensitivity of .857, .9, and .942 at distinguishing COVID-19, CAP, and normal individuals respectively, to rank first in the evaluation. Code and model weights are available at ~\url{https://github.com/shubhamchaudhary2015/ct_covid19_cap_cnn} |
