InRFNet: Involution Receptive Field Network for COVID-19 Diagnosis

COVID-19 is an emerging infectious disease that has been rampant worldwide since its onset causing Lung irregularity and severe respiratory failure due to pneumonia. The Community-Acquired Pneumonia (CAP), Normal, and COVID-19 Computed Tomography (CT) scan images are classified using Involution Rece...

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Bibliographic Details
Published in:Journal of physics. Conference series 2022-01, Vol.2161 (1), p.12064
Main Authors: Dhruv, M, Sai Chandra Teja, R, Sri Devi, R, Nagesh Kumar, S
Format: Article
Language:English
Subjects:
Computed tomography
Coronaviruses
COVID-19
Feature extraction
Feature maps
Image acquisition
Infectious diseases
Owls
Performance measurement
Physics
Pneumonia
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Summary:COVID-19 is an emerging infectious disease that has been rampant worldwide since its onset causing Lung irregularity and severe respiratory failure due to pneumonia. The Community-Acquired Pneumonia (CAP), Normal, and COVID-19 Computed Tomography (CT) scan images are classified using Involution Receptive Field Network from Large COVID-19 CT scan slice dataset. The proposed lightweight Involution Receptive Field Network (InRFNet) is spatial specific and channel-agnostic with Receptive Field structure to enhance the feature map extraction. The InRFNet model evaluation results show high training (99%) and validation (96%) accuracy. The performance metrics of the InRFNet model are Sensitivity (94.48%), Specificity (97.87%), Recall (96.34%), F1-score (96.33%), kappa score (94.10%), ROC-AUC (99.41%), mean square error (0.04), and the total number of parameters (33100).
ISSN:1742-6588
1742-6596
DOI:10.1088/1742-6596/2161/1/012064