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InRFNet: Involution Receptive Field Network for COVID-19 Diagnosis
1st International Conference on Artificial Intelligence, Computational Electronics and Communication System, AICECS 2021 ; 2161, 2022.
Article in English | Scopus | ID: covidwho-1707274
ABSTRACT
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). © 2022 Institute of Physics Publishing. All rights reserved.
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Full text: Available Collection: Databases of international organizations Database: Scopus Language: English Journal: 1st International Conference on Artificial Intelligence, Computational Electronics and Communication System, AICECS 2021 Year: 2022 Document Type: Article

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Full text: Available Collection: Databases of international organizations Database: Scopus Language: English Journal: 1st International Conference on Artificial Intelligence, Computational Electronics and Communication System, AICECS 2021 Year: 2022 Document Type: Article