Detection of COVID-19 infected lungs from chest x-ray using wavelet transform, HOG features extraction and SVM

Coronavirus SARS-CoV-2 referred to as COVID-19, is a both spreadable and infectious disease, which has footprinted a global pandemic and still infecting millions across the globe. At present, COVID-19 has made a devastating impact on our daily life. To detect coronavirus, some medical radiography technique is prominent such as chest X-ray images. This work represented the distinguishing features between normal and COVID-19 infected chest X-ray images through Discrete Wavelet transform (DWT) and Histogram of Oriented Gradients (HOG) methods which helps to indicate whether the person is COVID positive or negative. DWT and HOG transformations were performed to extract the features from the chest x-ray images. Support Vector Machine (SVM) classifier is used to the chest x-ray images for model training and validation. To evaluate the performance of the model accuracy, sensitivity, specificity and precision were calculated. DWT-SVM model provides the accuracy of 98.58%, the sensitivity of 98.38%, the specificity of 98.47% and the precision of 98.48% whereas the HOG-SVM model provides the accuracy of 99.39%, the sensitivity of 99.19%, the specificity 99.28% and precision 99.29%. So, the result indicates that the HOG-SVM model shows better performance than the DWT-SVM model. The experimental results may help the medical personnel to diagnose easily and to take the necessary steps for better treatment.