Optimal Ensemble learning model for COVID-19 detection using chest X-ray images

COVID-19 pandemic is the main outbreak in the world, which has shown a bad impact on people's lives in more than 150 countries. The major steps in fighting COVID-19 are identifying the affected patients as early as possible and locating them with special care. Images from radiology and radiography are among the most effective tools for determining a patient's ailment. Recent studies have shown detailed abnormalities of affected patients with COVID-19 in the chest radiograms. The purpose of this work is to present a COVID-19 detection system with three key steps: (i) preprocessing, (ii) Feature extraction, (iii) Classification. Originally, the input image is given to the preprocessing step as its input, extracting the deep features and texture features from the pre-processed image. Particularly, it extracts the deep features by inceptionv3. Then, the features like proposed Local Vector Patterns (LVP) and Local Binary Pattern (LBP) are extracted from the preprocessed image. Moreover, the extracted features are subjected to the proposed ensemble model based classification phase, including Support Vector Machine (SVM), Convolutional Neural Network (CNN), Optimized Neural Network (NN), and Random Forest (RF). A novel Self Adaptive Kill Herd Optimization (SAKHO) approach is used to properly tune the weight of NN to improve classification accuracy and precision. The performance of the proposed method is then compared to the performance of the conventional approaches using a variety of metrics, including recall, FNR, MCC, FDR, Thread score, FPR, precision, FOR, accuracy, specificity, NPV, FMS, and sensitivity, accordingly.

Automated summary

The COVID-19 pandemic has had a detrimental impact on people's lives worldwide. This study presents a COVID-19 detection system with three key steps: preprocessing, feature extraction, and classification. Various deep learning and texture feature extraction methods are applied, and multiple classification models, including SVM, CNN, NN, and RF, are used for accurate and precise classification.