Designing Hybrid CNN-SVM Model for COVID-19 Classification Based on X-ray Images Using LGBM Feature Selection

Abstract

COVID-19 still exists at an alarming level; hence, early diagnosis is important for treating and controlling this disease due to its rapid spread. The use of X-rays in medical image analysis can play an essential role in fast and affordable diagnosis. This study used a two-level feature selection in hybrid deep convolutional features obtained from the extraction of X-ray images. The transfer learning-based approach was implemented using five convolutional neural networks (CNNs) named VGG16, VGG19, ResNet50, InceptionV3, and Xception. The combination of two or three CNNs' performance as a feature extractor was then carefully analyzed. We selected the features obtained from multiple CNNs in a particular layer with a specified percentage of features in the first level for getting relevant features from various models. Then, we combined those features and did the second level of feature selection to select the most informative features. Both levels of feature selection were carried out using the light gradient boosting machine (LightGBM) algorithm. The final feature set has been used to classify COVID-19 and non-COVID-19 chest X-ray images using the support vector machines (SVM) classifier. The proposed model's performance was evaluated and analyzed on the open-access dataset. The highest accuracy was 99.80% using only 5% of the features extracted from ResNet50 and Xception. The other way of combining the ensemble of deep features and a few recent works for the classification of COVID-19 were also compared with the proposed model. As a result, our proposed model has achieved the best success rate for this dataset and may be deployed to support decision systems for radiologists.