期刊名称：International Journal of Advances in Engineering and Management
电子版ISSN：2395-5252
出版年度：2021
卷号：3
期号：5
页码：141-146
DOI：10.35629/5252-0304605607
语种：English
出版社：IJAEM JOURNAL
摘要：Covid-19 is severe disease issue where a large number of people lose their lives every day. This disease affects not only a single country and even the whole world suffered this virus disease. In past decade, several kinds of viruses (like COPD, SARS, MERS and etc.) came into the picture. In the present time, the whole world is affected by Covid19 disease and the most important thing is no single country scientists can prepare a vaccine for the same. COVID-19 is an infectious disease has spread all over the globe and is declared a pandemic. The detection of COVID-19 from chest X-ray and its differentiation from lung diseases with identical opacities is a puzzling task that relies on the availability of expert radiologists. Deep learning is an extremely powerful tool for learning complex, cognitive problems, to identify the diseases. In the present study, we have made use of a deep learning algorithm using the convolutional neural network (CNN) that can efficiently detect COVID-19 from chest X-ray images for swift diagnosis. Due to data scarcity related to COVID-19 chest X-ray images, instead of training the model from scratch, the present study made use of the Sparse matrix Construction are already available models in solving the analogous problems. Deep learning based classification models trained through the transfer learning approach can efficiently classify the chest X-ray images representing studied diseases. The analysis of this collected data is done with the help of CNN, a machine learning tool. This work mainly focuses on the use of CNN models for classifying chest X-ray images for coronavirus infected patients.
