Covid-19 Classification Using HOG-SVM and Deep Learning Models

Abstract

COVID-19 is measured as the biggest hazardous and fast infectious grief for the human body which has a severe impact on lives, health, and the community all over the world. It is still spreading throughout the world with different variants which is silently killing many lives globally. Thus, earlier diagnosis and accurate detection of COVID-19 cases are essential to protect global lives. Diagnosis COVID-19 through chest X-ray images is one of the best solutions to detect the virus in the infected person properly and quickly at a low cost. Encouraged by the existing research, in this paper, we proposed a hybrid model to classify the Covid cases and non-Covid cases with chest X-ray images based on feature extraction, machine learning and deep learning techniques. Two feature extractors, Histogram Oriented Gradient (HOG) and CNN (MobileNetV2, Sequential, ResNet152V2) are used to train the model. For the classification, we utilized two approaches: Support Vector Machine (SVM) for machine learning and CNN (MobileNetV2, Sequential, ResNet152V2) classifiers for deep learning. The experimental result analysis shows that the Sequential model and the ResNet152V2 model achieve 100% and 82.6% accuracy respectively which is satisfactory. On the other hand, the HOG-SVM method successfully detects all the test images correctly which provides the best result with 100% accuracy, specificity, and responsiveness over a limited public dataset.