Among the many countries of the world, the United States of America, Brazil, and India are the worst hit countries. This virus was first detected in Wuhan, China, from where it spread to the rest of the world. It is a disease spreading like wildfire throughout the world for which currently there is no cure. Recommended threshold values which yielded a high recall and accuracy were obtained for each dataset.ĬOVID-19, short for Coronavirus Disease 2019, is a life-threatening disease caused by Severe Acute Respiratory Syndrome Corona Virus (SARS-CoV-2). In relevance to COVID-19, as the recall is more important than precision, the trade-offs between recall and precision at different thresholds were explored.
The proposed model achieved uniformly good performance on five different datasets, consisting of chest CT scans and chest x-rays images. The performance of the proposed model was compared with two other ensemble models, baseline pre-trained computer vision models and existing models for COVID-19 detection. Five different chest CT scans and chest x-ray images were used to train and evaluate the proposed model. After an exhaustive search, three best-performing diverse models were selected to design a weighted average-based heterogeneous stacked ensemble. From each pre-trained model, the potential candidates for base classifiers were obtained by varying the number of additional fully-connected layers. Four pre-trained DL models were considered: Visual Geometry Group (VGG 19), Residual Network (ResNet 101), Densely Connected Convolutional Networks (DenseNet 169) and Wide Residual Network (WideResNet 50 2). The proposed model is a stacked ensemble of heterogenous pre-trained computer vision models. This paper proposes a novel stacked ensemble to detect COVID-19 either from chest CT scans or chest x-ray images of an individual. Interested readers can easily use these new algorithms with the aid of the R package lphom.One of the promising methods for early detection of Coronavirus Disease 2019 (COVID-19) among symptomatic patients is to analyze chest Computed Tomography (CT) scans or chest x-rays images of individuals using Deep Learning (DL) techniques. We use a unique dataset with almost 500 elections, where the real transfer matrices are known, to assess their accuracy. The new algorithms place the linear programming approach once again in a prominent position in the ecological inference toolkit. In addition to generating estimates for local ecological inference contingency tables and amending the tendency to produce extreme transfer probability estimates previously observed in other mathematical programming procedures, they prove to be quite competitive and more accurate than the current linear programming baseline algorithm. These two new algorithms represent an important step forward in the ecological inference mathematical programming literature.
Based on this and the homogeneity hypothesis, we suggest two new ecological inference algorithms. For the first time in the literature, a procedure based on linear programming is proposed to attain estimates of local contingency tables. From the mathematical programming framework, this paper suggests a new direction for tackling this problem. The estimation of RxC ecological inference contingency tables from aggregate data defines one of the most salient and challenging problems in the field of quantitative social sciences.
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