2022
DOI: 10.1016/j.compbiomed.2021.105127
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Transfer learning based novel ensemble classifier for COVID-19 detection from chest CT-scans

Abstract: Coronavirus Disease 2019 (COVID-19) is a deadly infection that affects the respiratory organs in humans as well as animals. By 2020, this disease turned out to be a pandemic affecting millions of individuals across the globe. Conducting rapid tests for a large number of suspects preventing the spread of the virus has become a challenge. In the recent past, several deep learning based approaches have been developed for automating the process of detecting COVID-19 infection from Lung Computerized Tomography (CT)… Show more

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Cited by 74 publications
(37 citation statements)
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“…Some literatures have reported that DNNs have achieved competitive performance in lung cancer [ 27 , 28 ], corneal endothelial cells [ 29 ], pancreas [ 30 , 31 ], polyp segmentation [ 32 , 33 ], etc. But, they are always prone to overfitting [ 34 , 35 ], poor robustness [ 36 ] and lack of generalization [ [37] , [38] , [39] ]. Furthermore, the lesion area of COVID-19 is usually small and blurred in CXR images, which needs us to pay more attention to these critical regions.…”
Section: Introductionmentioning
confidence: 99%
“…Some literatures have reported that DNNs have achieved competitive performance in lung cancer [ 27 , 28 ], corneal endothelial cells [ 29 ], pancreas [ 30 , 31 ], polyp segmentation [ 32 , 33 ], etc. But, they are always prone to overfitting [ 34 , 35 ], poor robustness [ 36 ] and lack of generalization [ [37] , [38] , [39] ]. Furthermore, the lesion area of COVID-19 is usually small and blurred in CXR images, which needs us to pay more attention to these critical regions.…”
Section: Introductionmentioning
confidence: 99%
“…The MTL achieved accuracies of 90.23% and 79.20% for detecting COVID-19 based on CT and RT-PCR. Shaik and Cherukuri [ 29 ] introduced a novel ensemble DNN strategy that used various TL-based pre-trained models for COVID-19 diagnosis based on CCT images. The strategy steps are as follows: preprocessing the CT images, feature extraction using the deep pre-trained models, fine-tuning the obtained features on a three-layered DNN, and classification via ensemble classifier.…”
Section: Related Studiesmentioning
confidence: 99%
“…Several DNN models have been developed for COVID-19 detection. In this respect, the CNN is the most frequently used method for COVID-19 ECG and CT images classification in comparison with other DNN-based models [12][13][14] [15][16] [17]. Concerning the classification of images, the proposed model is proved to be useful for detecting normal, COVID-19 and pneumonia cases.…”
Section: Related Workmentioning
confidence: 99%
“…The method reaches 87.5% of accuracy. ResNet50 is commonly used by researchers for feature extraction and classification of ECG and CT scan images [12][13][15] [16]. Metrics like accuracy, sensitivity, F1-score and specificity are essentially used for evaluating the performance of the classifier.…”
Section: Related Workmentioning
confidence: 99%