A Novel Deep Learning and Ensemble Learning Mechanism for Delta-Type COVID-19 Detection

Abstract

Recently, the novel coronavirus disease 2019 (COVID-19) has posed many challenges to the research community by presenting grievous severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that results in a huge number of mortalities and high morbidities worldwide. Furthermore, the symptoms-based variations in virus type add new challenges for the research and practitioners to combat. COVID-19-infected patients comprise trenchant radiographic visual features, including dry cough, fever, dyspnea, fatigue, etc. Chest X-ray is considered a simple and non-invasive clinical adjutant that performs a key role in the identification of these ocular responses related to COVID-19 infection. Nevertheless, the defined availability of proficient radiologists to understand the X-ray images and the elusive aspects of disease radiographic replies to remnant the biggest bottlenecks in manual diagnosis. To address these issues, the proposed research study presents a hybrid deep learning model for the accurate diagnosing of Delta-type COVID-19 infection using X-ray images. This hybrid model comprises visual geometry group 16 (VGG16) and a support vector machine (SVM), where the VGG16 is accustomed to the identification process, while the SVM is used for the severity-based analysis of the infected people. An overall accuracy rate of 97.37% is recorded for the assumed model. Other performance metrics such as the area under the curve (AUC), precision, F-score, misclassification rate, and confusion matrix are used for validation and analysis purposes. Finally, the applicability of the presumed model is assimilated with other relevant techniques. The high identification rates shine the applicability of the formulated hybrid model in the targeted research domain.

Keywords: AI; Delta-type COVID-19; VGG16; ensemble learning technique; hybrid deep learning.

Figure 1

Experimental setup of the proposed study.

Figure 2

A stepwise solution was followed to implement the proposed hybrid diagnosing model.

Figure 3

Performance results of the suggested hybrid deep learning technique.

Figure 4

Evolution of the suggested model using different training and test sets.

Figure 5

The area under the curve (AUC) and accuracy values are based on a varying number of hidden layers.

Figure 6

The confusion matrix reflects the recognition capabilities of the proposed research study.

Figure 7

Confusion matrix for severity-based analysis of the infected patients.

Figure 8

Evolution of the proposed research model.

Figure 9

The AUC values-based performance analysis of this research model with convolutional neural network one-dimensional (CNN 1D Net), residual network (ResNet), and Dense Convolutional Network (DenseNet) models.