2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information
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Paper Detail

Paper IDCHLG-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
SessionCHLG-1: COVID-19 Diagnosis
LocationZoom
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
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}