ArticleComputers in biology and medicine2021
A stacked ensemble for the detection of COVID-19 with high recall and accuracy.
Article in Computers in biology and medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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10 citing papers in PubMed.
- MssNet: An Efficient Spatial Attention Model for Early Recognition of Alzheimer's Disease.IEEE transactions on emerging topics in computational intelligence · 2025Article
- Article
- Evaluating deep learning techniques for identifying tongue features in subthreshold depression: a prospective observational study.Frontiers in psychiatry · 2024Article
- A comprehensive review of COVID-19 detection with machine learning and deep learning techniques.Health and technology · 2023Article
- Ensemble deep honey architecture for COVID-19 prediction using CT scan and chest X-ray images.Multimedia systems · 2023Article
- A multi-class classification framework for disease screening and disease diagnosis of COVID-19 from chest X-ray images.Multimedia tools and applications · 2023Article
- Deep learning-based important weights-only transfer learning approach for COVID-19 CT-scan classification.Applied intelligence (Dordrecht, Netherlands) · 2023Article
- Classification and Detection of COVID-19 and Other Chest-Related Diseases Using Transfer Learning.Sensors (Basel, Switzerland) · 2022Article
- A lightweight CNN-based network on COVID-19 detection using X-ray and CT images.Computers in biology and medicine · 2022Article
- Deep Ensemble Learning-Based Models for Diagnosis of COVID-19 from Chest CT Images.Healthcare (Basel, Switzerland) · 2022Article
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2 authors.
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Abstract
The main challenges for the automatic detection of the coronavirus disease (COVID-19) from computed tomography (CT) scans of an individual are: a lack of large datasets, ambiguity in the characteristics of COVID-19 and the detection techniques having low sensitivity (or recall). Hence, developing diagnostic techniques with high recall and automatic feature extraction using the available data are crucial for controlling the spread of COVID-19. This paper proposes a novel stacked ensemble capable of detecting COVID-19 from a patient's chest CT scans with high recall and accuracy. A systematic approach for designing a stacked ensemble from pre-trained computer vision models using transfer learning (TL) is presented. A novel diversity measure that results in the stacked ensemble with high recall and accuracy is proposed. The stacked ensemble proposed in this paper considers four pre-trained computer vision models: the visual geometry group (VGG)-19, residual network (ResNet)-101, densely connected convolutional network (DenseNet)-169 and wide residual network (WideResNet)-50-2. The proposed model was trained and evaluated with three different chest CT scans. As recall is more important than precision, the trade-offs between recall and precision were explored in relevance to COVID-19. The optimal recommended threshold values were found for each dataset.
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