International Journal of Neutrosophic Science IJNS 2690-6805 2692-6148 10.54216/IJNS https://www.americaspg.com/journals/show/568 2020 2020 Department of Mathematics and Computer Sciences, Faculty of Sciences, Port Said University, Egypt admin admin Member of the Egyptian inventors Syndicate, and the Arab Invention Development Authority, Egypt Mohamed Fazaa Member of the Egyptian inventors Syndicate, and the Arab Invention Development Authority, Egypt Mohamed Yahya Misr Higher Institute for Commerce and Computers, M.E.T Academy, Mansoura, Egypt M. Kazim The idea for this paper is based on the use of a computer-connected microscope associated with Deep Learning, using Convolutional Neural Network (CNN). CNN is a mathematical type of Deep Learning used to recognize and diagnose images.  After that, we photograph blood samples, as well as samples, were taken from the mouth and nose, as well as it is possible to photograph the throat from the inside of a large number of injured and uninfected people as well as suspected of infection and provide a large number of references for this program for each type of those different samples. It is possible to perform this process in few minutes, save time and money, make analyzes for the largest possible number of people, and provide results in an accurate and documented manner, which is through the Neutrosophic time series. The basis and analysis of dealing with all data, whether specific or not, that can be taken by time series values, then we present the linear model for the neutrosophic time series, and we test the significance of its coefficient based on patients distribution. Finally, from the above, we can provide a patient neutrosophic time series according to the linear model through which we can accurately predict the program will give degrees of verification and degrees of the uncertainty of the data. 2020 2020 54 59 10.54216/IJNS.090105 https://www.americaspg.com/articleinfo/21/show/568