COVID-19 Detection Using Ensemble and Single Classifiers

For feature extraction task, we utilized the proposed CNN architecture, SIFT and MobileNetV2 model. We utilized machine learning classifiers to classify the extracted features as COVID-19 case or not. The machine learning classifier consumes numerical values, as extracted from the CNN model, rather than a CT scan image. We proposed comparing the performance of a single classifier and an ensemble learning classifier by classifying the extracted features from the feature extraction phase. We utilized random forest, an example for Bagging ensemble methods, which depends on a decision trees model as a base classifier. Boosting methods are applied also by utilizing a support vector machine (SVM) classifier as a weak learner. On the other hand, a single classifier, logistic regression, is used to classify the extracted features in the previous stage.
The standard MobileNetV2 model extracts 62,720 numerical values with shape 7 × 7 × 1,280 of the CT scan image; these features are used as an input to the classification models. MobileNetV2 model with the fine-tuned model on top of it extracts 128 numerical values of the CT scan image for the classification process, as listed in Table 2. The proposed CNN model extracts 32 features from the CT scan image that are used as input for the classification task. As the proposed CNN model achieves better results than the pre-trained MobileNetV2 model, we were encouraged to merge the features extracted from proposed CNN and SIFT to together. As listed in Table 2, utilizing SIFT features with our proposed CNN features achieves the superior results.
Several metrics can evaluate the performance of a classifier. In the following, we explain the utilized metrics for evaluating the proposed classifier. Besides, we mean by True Positive (TP) the outcome where the model correctly predicts the positive class, e.g., positive COVID-19 patients diagnosed as COVID-19 (+). A True Negative (TN) is a results where the model predicts the negative class flawlessly, e.g., a CT scan image of COVID-19 patient is diagnosed as a negative COVID-19 (-) case. A False Positive (FP) case where the model incorrectly forecasts the positive class, e.g., patients suffering from other lung diseases and incorrectly classified as COVID-19 (+). False Negative is a case where the model incorrectly forecasts the negative class, e.g., patients infected by COVID-19 (+) and incorrectly classified as COVID-19 (-). Accuracy is defined as the fraction of correct predictions (both True Positive and True Negative).
The first evaluation metric is precision; where can it can be defined as the rate of TP outcomes to the total number of positive outcomes (TP + FP), as shown in Eq 2.
$$\begin{array}{c}\hfill Precision=\frac{TP}{TP+FP}\end{array}$$
The second evaluation metric is the recall; it is the ratio of TP outcomes to the actual number of positive samples (TP + FN), as shown in Eq 3.
$$\begin{array}{c}\hfill Recall=\frac{TP}{TP+FN}\end{array}$$
The third evaluation metric is the F1-score; it is the harmonic mean of precision and recall, as shown in Eq 4.
$$\begin{array}{c}\hfill F1-score=\frac{TP}{TP+0.5(FP+FN)}\end{array}$$
The fourth evaluation metric is the specificity; It is the ratio of TN outcomes to the total negative outcomes (TP + FN) of the model, as shown in Eq 5.
$$\begin{array}{c}\hfill Specificity=\frac{TN}{TN+FP}\end{array}$$
The fifth evaluation metric is the accuracy; It is the ratio of correct outcomes (TP + TN) to the total number of outcomes, as shown in Eq 6.
$$\begin{array}{c}\hfill Accuracy=\frac{TP+TN}{TP+TN+FP+FN}\end{array}$$