Hybrid ensemble model for differential diagnosis between COVID-19 and common viral pneumonia by chest X-ray radiograph

Abstract

Background: Chest X-ray radiography (CXR) has been widely considered as an accessible, feasible, and convenient method to evaluate suspected patients' lung involvement during the COVID-19 pandemic. However, with the escalating number of suspected cases, traditional diagnosis via CXR fails to deliver results within a short period of time. Therefore, it is crucial to employ artificial intelligence (AI) to enhance CXRs for obtaining quick and accurate diagnoses. Previous studies have reported the feasibility of utilizing deep learning methods to screen for COVID-19 using CXR and CT results. However, these models only use a single deep learning network for chest radiograph detection; the accuracy of this approach required further improvement.

Methods: In this study, we propose a three-step hybrid ensemble model, including a feature extractor, a feature selector, and a classifier. First, a pre-trained AlexNet with an improved structure extracts the original image features. Then, the ReliefF algorithm is adopted to sort the extracted features, and a trial-and-error approach is used to select the n most important features to reduce the feature dimension. Finally, an SVM classifier provides classification results based on the n selected features.

Results: Compared to five existing models (InceptionV3: 97.916 ± 0.408%; SqueezeNet: 97.189 ± 0.526%; VGG19: 96.520 ± 1.220%; ResNet50: 97.476 ± 0.513%; ResNet101: 98.241 ± 0.209%), the proposed model demonstrated the best performance in terms of overall accuracy rate (98.642 ± 0.398%). Additionally, compared to the existing models, the proposed model demonstrates a considerable improvement in classification time efficiency (SqueezeNet: 6.602 ± 0.001s; InceptionV3: 12.376 ± 0.002s; ResNet50: 10.952 ± 0.001s; ResNet101: 18.040 ± 0.002s; VGG19: 16.632 ± 0.002s; proposed model: 5.917 ± 0.001s).

Conclusion: The model proposed in this article is practical and effective, and can provide high-precision COVID-19 CXR detection. We demonstrated its suitability to aid medical professionals in distinguishing normal CXRs, viral pneumonia CXRs and COVID-19 CXRs efficiently on small sample sizes.

Keywords: COVID-19; Dimension reduction; Transfer learning; X-ray imaging.

Graphical abstract

Fig. 1

Examples of three types of samples in the dataset: (A) Normal; (B) Viral pneumonia; (C) COVID-19.

Fig. 2

An overview of model architecture: a transferred AlexNet for feature extraction, the ReliefF algorithm for feature selection and an SVM classifier.

Fig. 3

Structure of AlexNet used in this work.

Fig. 4

The training curve of the model.

Fig. 5

Classification accuracy of SVM classifier with different numbers of input features.

Fig. 6

Structures of models used in self-contrast study: (A) original AlexNet, (B) improved AlexNet, (C) improved AlexNet + SVM, (D) improved AlexNet + ReliefF + SVM.

Fig. 7

Classification results of four models: (A) Original AlexNet, (B) Improved AlexNet, (C) Improved AlexNet + SVM, and (D) Proposed model (improved AlexNet + ReliefF + SVM). The results displayed in this figure correspond to the results with the highest classification accuracy of each model.

Fig. 8

Confusion matrices of four models: (A) Original AlexNet (95.98%), (B) Improved AlexNet (98.09%), (C) Improved AlexNet + SVM (98.47%), and (D) Proposed model (improved AlexNet + ReliefF + SVM) (99.43%) The results displayed in the confusion matrix correspond to the results with the highest classification accuracy of each model.

Fig. 9

Confusion matrix of (A) The proposed model (improved AlexNet + ReliefF + SVM) (99.43%); (B) InceptionV3 (98.47%); (C) SqueezeNet (97.51%); (D) ResNet-50 (97.90%); (E) ResNet-101 (98.27%); and (F) VGG19 (97.32%). The results displayed in the confusion matrix correspond to the results with the highest classification accuracy of each model.

Fig. 10

Precision-recall curves of (1) proposed model (improved AlexNet + ReliefF + SVM); (2) InceptionV3; (3) SqueezeNet; (4) ResNet50; (5) ResNet101; and (6) VGG19: (A) Overall; (B) Magnified.

Fig. 11

Comparative result: performances of proposed method (improved AlexNet + ReliefF + SVM), SqueezeNet, InceptionV3, ResNet50, ResNet101 and VGG19 (n = 40).

Fig. 12

ROC curves of (A) improved AlexNet + ReliefF + SVM, (B) SqueezeNet, (C) InceptionV3, (D) VGG19, (E) ResNet50 and (E) ResNet101 when true positive results are defined as that COVID-19 samples are accurately recognized.

Fig. 13

Possible components in the proposed model structure.

Fig. 14

Confusion matrix of (A) InceptionV3+ReliefF + SVM (98.27%); (B) SqueezeNet + ReliefF + SVM (98.09%); (C) AlexNet + ReliefF + Random Forest (98.66%); (D) AlexNet + ReliefF + ELM (99.04%); (E) AlexNet + PSO + SVM (99.04%); (F) AlexNet + mutInfFS + SVM (98.85%).

Fig. 15

Classification performances of models (A) using different feature extractors; (B) using different feature selectors; (C) using different final classifiers.