期刊名称：Journal of Theoretical and Applied Information Technology
印刷版ISSN：1992-8645
电子版ISSN：1817-3195
出版年度：2021
卷号：99
期号：23
语种：English
出版社：Journal of Theoretical and Applied
摘要：To stop the fast-spreading of covid19, there needs to be a significant improvement in the speed with which the diagnosis is performed. Many studies have been done on using deep learning algorithms like convolutional neural networks and many of its variants available in the industry to make the diagnosis faster. However, most of these approaches involve using datasets that are not that compatible with the real world. In this paper, we will be using efficient techniques to address this problem by using CT scans and leveraging most of the features available in CT-scan images to build a model that can classify whether covid19 infects a person or not, given his CT scan as input to the model. As CT scan images are more reliable and can represent the condition of a person in a more detailed way than any other images like X-rays, these can be used for obtaining faster and precise results.
