Automatic COVID-19 Detection from Chest X-Rays using Deep Learning Techniques

Abstract

COVID-19 emerged in November 2019 in the Wuhan city of China. Since then, it has expanded exponentially and reached every corner of the world. To date, it has infected more than three hundred eighty-five million people and caused more than five million seven hundred deaths. Traditional COVID-19 diagnostic tests lack sensitivity and result in false-negative reports several times. Using X-Rays and CT scans to detect covid-19 can aid the diagnosis process when powered by deep learning techniques. Using deep learning will provide accurate results in a fast and automatic manner. The proposed research work has performed a total of twenty-eight experiments. This research work has experimented with seven different Deep Learning models including, DenseNet201, MobileNetV2, DenseNet121, VGG16, VGG19, InceptionV3, and ResNet50. The performance of each model is tested based on the distinct image enhancement techniques. The four different experiments include raw data, data preprocessed with gamma correction for two different gamma values (0.7 and 1.2), and Contrast Limited Adaptive Histogram Equalization (CLAHE). Gamma Correction with gamma value 1.2 performed the best. Lastly, this research work has created an ensemble of three best-performing algorithms including, DenseNet201, MobileNetV2, DenseNet121, and achieved an accuracy, precision, recall, f1 score, and AUC of 98.34%, 98.61%, 98.78%, 98.2%, and 99.8%, respectively.