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.

The Applications of Artificial Intelligence in Chest Imaging of COVID-19 Patients: A Literature Review

“…Various authors also investigated the effectiveness of supervised AI learning models in aiding medical professionals in the differential diagnosis between COVID-19 pneumonia and other lung diseases, in particular the non-COVID-19 viral pneumonia, with a reported accuracy of up to 87% [33,39,41,42,63,64]. Jin et al proposed a three-step hybrid model, incorporating a feature extractor, feature selector, and an SVM classifier, reporting an overall accuracy rate of 98.6%, with a remarkable reduction of training time and of the training sets size [65].…”

Medical imaging and computational image analysis in COVID-19 diagnosis: A review

“… Karakanis and Leontidis (2021) [ 259 ] Automatic COVID-19 diagnosis CXR CNN and GAN 435 145 98.7 The GAN model has been used for data augmentation. Jin et al (2021) [ 260 ] Automatic COVID-19 diagnosis CXR CNN 1743 543 98.64 A hybrid ensemble model, including a pre-trained AlexNet as feature extractor and an SVM classifier as the classifier, has been proposed. Ahmad et al (2021) [ 261 ] Automatic COVID-19 diagnosis CXR CNN 4000 1000 98.45 Some of the existing CNN architectures with data augmentation have been used for COVID-19 diagnosis.…”

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

“…[ 64 ]; [ 65 ]; [ 66 ]; [ 67 ]; [ 68 ]; [ 69 ]; [ 70 ]; [ 71 ]; [ 72 ]; [ 73 ]; [ 74 ]; [ 75 ]; [ 76 ]; [ 77 ]; [ 78 ]; [ 79 ]; [ 80 ]; [ 81 ]; [ 82 ]; [ 83 ]; [ 84 ]; [ 85 ]; [ 86 ]; [ 87 ]; [ 88 ]; [ 89 ]; [ 90 ]; [ 91 ]; [ 92 ]; [ 93 ]; [ 94 ].…”