Automated detection of COVID-19 from CT scan using convolutional neural network

• Autorzy: Mishra Narendra Kumar , Singh Pushpendra , Joshi Shiv Dutt

Identyfikatory

DOI

10.1016/j.bbe.2021.04.006

• Języki publikacji: EN

Abstrakty

EN

Under the prevailing circumstances of the global pandemic of COVID-19, early diagnosis and accurate detection of COVID-19 through tests/screening and, subsequently, isolation of the infected people would be a proactive measure. Artificial intelligence (AI) based solutions, using Convolutional Neural Network (CNN) and exploiting the Deep Learning model’s diagnostic capabilities, have been studied in this paper. Transfer Learning approach, based on VGG16 and ResNet50 architectures, has been used to develop an algorithm to detect COVID-19 from CT scan images consisting of Healthy (Normal), COVID-19, and Pneumonia categories. This paper adopts data augmentation and fine-tuning techniques to improve and optimize the VGG16 and ResNet50 model. Further, stratified 5-fold cross-validation has been conducted to test the robustness and effectiveness of the model. The proposed model performs exceptionally well in case of binary classification (COVID-19 vs. Normal) with an average classification accuracy of more than 99% in both VGG16 and ResNet50 based models. In multiclass classification (COVID-19 vs. Normal vs. Pneumonia), the proposed model achieves an average classification accuracy of 86.74% and 88.52% using VGG16 and ResNet50 architectures as baseline, respectively. Experimental results show that the proposed model achieves superior performance and can be used for automated detection of COVID-19 from CT scans.

Słowa kluczowe

EN

COVID-19; convolutional neural network; transfer learning; VGG16 model; ResNet50; CT scan image

PL

COVID-19; sieć neuronowa konwolucyjna; transfer wiedzy; VGG16; ResNet50

• Wydawca: Nałęcz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences ; Elsevier
• Czasopismo: Biocybernetics and Biomedical Engineering
• Rocznik: 2021
• Tom: Vol. 41, no. 2
• Strony: 572--588
• Opis fizyczny: Bibliogr. 53 poz., rys., tab., wykr.

Twórcy

autor

Mishra Narendra Kumar

• Department of Electrical Engineering, Indian Institute of Technology Delhi, India

autor

Singh Pushpendra

• Department of ECE, NIT Hamirpur, India

autor

Joshi Shiv Dutt

• Department of Electrical Engineering, Indian Institute of Technology Delhi, India

Bibliografia

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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).