A multi-class classification framework for disease screening and disease diagnosis of COVID-19 from chest X-ray images.

To accurately diagnose multiple lung diseases from chest X-rays, the critical aspect is to identify lung diseases with high sensitivity and specificity. This study proposed a novel multi-class classification framework that minimises either false positives or false negatives that is useful in computer aided diagnosis or computer aided detection respectively. To minimise false positives or false negatives, we generated respective stacked ensemble from pre-trained models and fully connected layers using selection metric and systematic method. The diversity of base classifiers was based on diverse set of false positives or false negatives generated. The proposed multi-class framework was evaluated on two chest X-ray datasets, and the performance was compared with the existing models and base classifiers. Moreover, we used LIME (Local Interpretable Model-agnostic Explanations) to locate the regions focused by the multi-class classification framework.

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

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.

Automatic detection of COVID-19 from chest CT scan and chest X-Rays images using deep learning, transfer learning and stacking.

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. This paper proposes a novel stacked ensemble to detect COVID-19 either from chest CT scans or chest x-ray images of an individual. The proposed model is a stacked ensemble of heterogenous pre-trained computer vision models. 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). From each pre-trained model, the potential candidates for base classifiers were obtained by varying the number of additional fully-connected layers. After an exhaustive search, three best-performing diverse models were selected to design a weighted average-based heterogeneous stacked ensemble. Five different chest CT scans and chest x-ray images were used to train and evaluate the proposed model. 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. The proposed model achieved uniformly good performance on five different datasets, consisting of chest CT scans and chest x-rays images. 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. Recommended threshold values which yielded a high recall and accuracy were obtained for each dataset.

Deep learning-based improved snapshot ensemble technique for COVID-19 chest X-ray classification.

COVID-19 has proven to be a deadly virus, and unfortunately, it triggered a worldwide pandemic. Its detection for further treatment poses a severe threat to researchers, scientists, health professionals, and administrators worldwide. One of the daunting tasks during the pandemic for doctors in radiology is the use of chest X-ray or CT images for COVID-19 diagnosis. Time is required to inspect each report manually. While a CT scan is the better standard, an X-ray is still useful because it is cheaper, faster, and more widely used. To diagnose COVID-19, this paper proposes to use a deep learning-based improved Snapshot Ensemble technique for efficient COVID-19 chest X-ray classification. In addition, the proposed method takes advantage of the transfer learning technique using the ResNet-50 model, which is a pre-trained model. The proposed model uses the publicly accessible COVID-19 chest X-ray dataset consisting of 2905 images, which include COVID-19, viral pneumonia, and normal chest X-ray images. For performance evaluation, the model applied the metrics such as AU-ROC, AU-PR, and Jaccard Index. Furthermore, it also obtained a multi-class micro-average of 97% specificity, 95% f 1-score, and 95% classification accuracy. The obtained results demonstrate that the performance of the proposed method outperformed those of several existing methods. This method appears to be a suitable and efficient approach for COVID-19 chest X-ray classification.