Novel Feature Selection and Voting Classifier Algorithms for COVID-19 Classification in CT Images

Abstract

Diagnosis is a critical preventive step in Coronavirus research which has similar manifestations with other types of pneumonia. CT scans and X-rays play an important role in that direction. However, processing chest CT images and using them to accurately diagnose COVID-19 is a computationally expensive task. Machine Learning techniques have the potential to overcome this challenge. This paper proposes two optimization algorithms for feature selection and classification of COVID-19. The proposed framework has three cascaded phases. Firstly, the features are extracted from the CT scans using a Convolutional Neural Network (CNN) named AlexNet. Secondly, a proposed features selection algorithm, Guided Whale Optimization Algorithm (Guided WOA) based on Stochastic Fractal Search (SFS), is then applied followed by balancing the selected features. Finally, a proposed voting classifier, Guided WOA based on Particle Swarm Optimization (PSO), aggregates different classifiers' predictions to choose the most voted class. This increases the chance that individual classifiers, e.g. Support Vector Machine (SVM), Neural Networks (NN), k-Nearest Neighbor (KNN), and Decision Trees (DT), to show significant discrepancies. Two datasets are used to test the proposed model: CT images containing clinical findings of positive COVID-19 and CT images negative COVID-19. The proposed feature selection algorithm (SFS-Guided WOA) is compared with other optimization algorithms widely used in recent literature to validate its efficiency. The proposed voting classifier (PSO-Guided-WOA) achieved AUC (area under the curve) of 0.995 that is superior to other voting classifiers in terms of performance metrics. Wilcoxon rank-sum, ANOVA, and T-test statistical tests are applied to statistically assess the quality of the proposed algorithms as well.

Efficient deep neural networks for classification of COVID-19 based on CT images: Virtualization via software defined radio

“…In [20] , ResNet-50 was used and accuracy was obtained equal to that of ResNet-50 and less than that of other networks in our study. The accuracy of ResNet-50 used in [21] was 15% less than the accuracy of that used in our research.…”

“…Similar to our study, [32] and [30] have also used the same dataset. In [20] , [21] and [23] , ResNet-50 and CNN neural networks have been used and in [31] , only CNN has been used.

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“…El-Kenawy et al. [21] have focused of experiments on three scenarios to evaluate accuracy and performance of the suggested framework for the classification of COVID-19. In scenario I accuracies of many CNN models were compared on CT images from the COVID-19 dataset.…”

“… 0.769 ResNet-18 Spec. 0.799 ResNet-18 F1-score 0.789 ResNet-18 [21] The ResNet-50 neural network COVID-19: 684 Non-COVID: 254 Acc. 98% [22] The VGG19 neural network COVID-19: 349 Non-COVID: 397 Prec.…”

A Novel Intrusion Detection System for RPL Based IoT Networks with Bio-Inspired Feature Selection and Ensemble Classifier

“…• Accuracy: It measures the capability of the model to predict all the instances correctly as denoted in Equation (14). It is the count of correctly detected instances over the total detected in the test data.…”