The number of Covid-19 cases around the world has now topped 20 million. The Japanese mine has a large and large range of restrictions. As you observe, Covid-19 has indeed spread fast and wide across the world due to its ease of transmission, early detection and isolation of the infected is of utmost importance to contain the virus. Currently, the antibody tests, antigen tests and the RT PCR are the three most common ways to detect the person is a carrier of Covid-19. These tests while common are not without significant limitations, and the most significant of all is the fact that their false negative rates are not negligible. Coupled with its ease of transmission, even the few false negatives could be disastrous. An equally recent research has shown that chest x-rays could be potentially useful in dating with a precise and accurate diagnosis of Covid-19. Experience radiologists have managed to identify some specific patterns in chest x-rays which are indicative of Covid-19. However, identify these patterns requires trained radiologists, when you have potentially millions of infected in a pandemic situation, manpower is limited. But if you can train computers like determining if an x-ray is indicative of Covid-19, then we will be less bottleneck by our need for radiologists. To do this with traditional machine learning, this will require manual extraction of features for the model to use and classify these x-rays. However, for deep learning, the model can simultaneously learn to identify these features and classify their image. Okay, this has been done for a similar problem, detecting pneumonia through x-rays and successfully as well, which is certainly a great indicator that the same performance can be achieved for Covid-19 detection. If you were able to detect Covid-19 infection in radiographic images using deep learning at a comparable level of success, when coupled with the less sensitive tests mentioned previously, this would allow us to more effectively detect and isolate Covid-19 infected individuals. However, in order to do so, our model needs to be able to detect these specific patterns in x-rays which are indicative of Covid-19. These are three sample-expert images from our data set, each one belonging to a different category out of three possible categories, at a Covid-19, normal or viral pneumonia. But how does neural network interpret images? Most computer vision neural networks contain convolutional layers. These layers are what allows them to extract features from an image. The way these convolutional layers work is by looking at a window about the pixel in an image and looking at how these pixels relate to one another. Simple features such as cages and locks can be extracted by convolving an image's raw pixel intensity within appropriate filter. And by stacking these convolutional operations, we are able to hierarchically extract high-level features from the image. Indeed, learning the feature extracting layers I sold at the classification layer is about how successful they are, and learning the right filters needed for a good classification. However, this requires an immense amount of data in order for the neural network to learn how to expect these features. For the transfer learning, we can modify existing neural networks to learn similar concepts more efficiently in learning it from scratch. The problem we chose which is the dense net 161 was able to classify x-ray images from our custom data set, indicative of Covid-19, with accuracy of 0.9872, with a recall score of 0.9939 specifically for Covid, and a weighted F1 score of 0.9872.