A stacked ensemble for the detection of COVID-19 with high recall and accuracy

Jangam, Ebenezer; Annavarapu, Chandra Sekhara Rao

doi:10.1016/j.compbiomed.2021.104608

Cited by 32 publications

(20 citation statements)

References 86 publications

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“…We compared the performance of the proposed WAE method to the existing methods [ 25 , 28 , 29 , 32 , 33 , 41 ] on the respective datasets that were used to evaluate the existing methods. Our choice of these methods [ 25 , 28 , 29 , 32 , 33 , 41 ] for comparison was based on the dataset composition and the similarity of the experiments conducted. Accuracy, recall, precision, and F1-score were the evaluation metrics considered for the comparison.…”

Section: Resultsmentioning

confidence: 99%

“…As can be observed, the majority of the recent studies on COVID19 detection have relied on individual Deep Learning models e.g., AlexNet, VGG16, VGG19, ResNet50, and ResNet101 [ 28 , 38 , 40 ]. None of the studies attempted to combine the models in order to increase their detection capabilities except for one investigation by Ebenezer et al [ 41 ] which has proposed a stacked ensemble that includes four pre-trained CNN networks (VGG19, ResNet101, DenseNet169, and WideResNet50-2) to detect COVID-19. Their stacked ensemble system was generated using a similarity measure and a systematic approach.…”

Section: Related Workmentioning

confidence: 99%

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Deep Ensemble Learning-Based Models for Diagnosis of COVID-19 from Chest CT Images

et al. 2022

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Novel coronavirus (COVID-19) has been endangering human health and life since 2019. The timely quarantine, diagnosis, and treatment of infected people are the most necessary and important work. The most widely used method of detecting COVID-19 is real-time polymerase chain reaction (RT-PCR). Along with RT-PCR, computed tomography (CT) has become a vital technique in diagnosing and managing COVID-19 patients. COVID-19 reveals a number of radiological signatures that can be easily recognized through chest CT. These signatures must be analyzed by radiologists. It is, however, an error-prone and time-consuming process. Deep Learning-based methods can be used to perform automatic chest CT analysis, which may shorten the analysis time. The aim of this study is to design a robust and rapid medical recognition system to identify positive cases in chest CT images using three Ensemble Learning-based models. There are several techniques in Deep Learning for developing a detection system. In this paper, we employed Transfer Learning. With this technique, we can apply the knowledge obtained from a pre-trained Convolutional Neural Network (CNN) to a different but related task. In order to ensure the robustness of the proposed system for identifying positive cases in chest CT images, we used two Ensemble Learning methods namely Stacking and Weighted Average Ensemble (WAE) to combine the performances of three fine-tuned Base-Learners (VGG19, ResNet50, and DenseNet201). For Stacking, we explored 2-Levels and 3-Levels Stacking. The three generated Ensemble Learning-based models were trained on two chest CT datasets. A variety of common evaluation measures (accuracy, recall, precision, and F1-score) are used to perform a comparative analysis of each method. The experimental results show that the WAE method provides the most reliable performance, achieving a high recall value which is a desirable outcome in medical applications as it poses a greater risk if a true infected patient is not identified.

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Section: Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Deep Ensemble Learning-Based Models for Diagnosis of COVID-19 from Chest CT Images

et al. 2022

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“…The authors achieved 91.5% accuracy, 0.915 sensitivity, and 0.915 F-score. In addition, [ 43 ] also proposed a stacked ensemble method. The authors claimed that the stacked ensemble method achieved higher recall and accuracy.…”

Section: Experimental Setup and Resultsmentioning

confidence: 99%

“…The proposed model achieved 90.75% accuracy. Similarly, in another study [ 43 ], the authors proposed a stacked ensemble-based method and claimed higher recall. The proposed method achieved 94% accuracy and 0.98 recall.…”

Section: Related Workmentioning

confidence: 86%

Deep learning-based important weights-only transfer learning approach for COVID-19 CT-scan classification

et al. 2022

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COVID-19 has become a pandemic for the entire world, and it has significantly affected the world economy. The importance of early detection and treatment of the infection cannot be overstated. The traditional diagnosis techniques take more time in detecting the infection. Although, numerous deep learning-based automated solutions have recently been developed in this regard, nevertheless, the limitation of computational and battery power in resource-constrained devices makes it difficult to deploy trained models for real-time inference. In this paper, to detect the presence of COVID-19 in CT-scan images, an important weights-only transfer learning method has been proposed for devices with limited runt-time resources. In the proposed method, the pre-trained models are made point-of-care devices friendly by pruning less important weight parameters of the model. The experiments were performed on two popular VGG16 and ResNet34 models and the empirical results showed that pruned ResNet34 model achieved 95.47% accuracy, 0.9216 sensitivity, 0.9567 F-score, and 0.9942 specificity with 41.96% fewer FLOPs and 20.64% fewer weight parameters on the SARS-CoV-2 CT-scan dataset. The results of our experiments showed that the proposed method significantly reduces the run-time resource requirements of the computationally intensive models and makes them ready to be utilized on the point-of-care devices.

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“…The final accuracy was as high as 99.05%. Transfer learning was applied on VGG19, ResNet101, DenseNet169 and WideResNet50-2, followed by ensemble method to obtain accuracy of 93.50% on classification between COVID-19 and non-COVID-19 [ 29 ]. Abraham and Nair [ 30 ] use ensemble learning model combining five convolutional neural networks such as MobileNetV2, ShuffleNet, Xception, DarkNet53 and EfficientNet-B0 for feature extraction, and use the kernel support vector machine (Kernel support vector machine) as the classifier, using a total of 746 CT images.…”

Section: Introductionmentioning

confidence: 99%