Computer Based COVID-19 Detection and Classification

COVID-19 is a worldwide epidemic, as announced by the World Health Organization (WHO) in March 2020. Machine learning (ML) and Digital Image Processing methods can play vital roles in identifying COVID-19 patients by visually analyzing their chest x-ray images. In our project, a new ML method is proposed to classify the chest x-ray images into two classes, COVID-19 patient or non-COVID-19 person. ML has demonstrated high performance for several image processing applications such as image analysis, image classification, and image segmentation. Presently, there are two main methods for testing COVID-19 namely, Molecular test and Serological test. COVID-19 tests are new, and assessing their accuracy is challenging. PCR tests may produce false negatives. Apart from clinical procedures, machine learning will provide a lot of support in identifying the disease with the help of image data. The proposed method of COVID-19 x-ray image classification model begins by extracting the features from the input images, either COVID-19 or Non-COVID-19, using feature extraction technique and Machine Learning Algorithms are used to classify the chest X-ray images. In addition, our proposed model will also analyze the severity of the affected COVID-19 class. The proposed diagnosis will be cost-effective and more accurate than standard tests for COVID-19.