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• W3204450165 endingPage "104895" @default.
• W3204450165 startingPage "104895" @default.
• W3204450165 abstract "The COVID-19 pandemic has collapsed the public healthcare systems, along with severely damaging the economy of the world. The SARS-CoV-2 virus also known as the coronavirus, led to community spread, causing the death of more than a million people worldwide. The primary reason for the uncontrolled spread of the virus is the lack of provision for population-wise screening. The apparatus for RT-PCR based COVID-19 detection is scarce and the testing process takes 6-9 h. The test is also not satisfactorily sensitive (71% sensitive only). Hence, Computer-Aided Detection techniques based on deep learning methods can be used in such a scenario using other modalities like chest CT-scan images for more accurate and sensitive screening. In this paper, we propose a method that uses a Sugeno fuzzy integral ensemble of four pre-trained deep learning models, namely, VGG-11, GoogLeNet, SqueezeNet v1.1 and Wide ResNet-50-2, for classification of chest CT-scan images into COVID and Non-COVID categories. The proposed framework has been tested on a publicly available dataset for evaluation and it achieves 98.93% accuracy and 98.93% sensitivity on the same. The model outperforms state-of-the-art methods on the same dataset and proves to be a reliable COVID-19 detector. The relevant source codes for the proposed approach can be found at: https://github.com/Rohit-Kundu/Fuzzy-Integral-Covid-Detection." @default.
• W3204450165 created "2021-10-11" @default.
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• W3204450165 date "2021-11-01" @default.
• W3204450165 modified "2023-10-03" @default.
• W3204450165 title "COVID-19 detection from lung CT-Scans using a fuzzy integral-based CNN ensemble" @default.
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• W3204450165 doi "https://doi.org/10.1016/j.compbiomed.2021.104895" @default.
• W3204450165 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/8483997" @default.
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