Abstract Covid-19 is a contagious respiratory illness caused by a new coronavirus called SARSCOV-2, spreads all around the world and death rate increases at an exponential rate. Covid-19 can be diagnosed either by laboratory base approaches such as nucleic acid testing, antigens test and serology (antibody) tests or by medical imaging tools such as Xray and Computed Tomography (CT). RT-PCR remains the primary and gold standard for diagnosing Covid-19 but due to shortages of RT-PCR kit, CT images can be used as an alternative early detection toolkit of Covid-19 as a simpler, quicker and more reliable diagnosis of Covid-19. As increase in bandwidth of CT images as well as the new Covid19 virus consumes a lot of time and workload of the radiologist increases substantially. Deep Learning models can assist the radiologist by learning the features of Covid-19 by their annotated CT images. This paper proposes novel deep learning models for the three main tasks namely 1. Binary classification of Covid-19 2. Automatic lung segmentation and 3. Covid-19 region segmentation. The proposed deep learning models produce an accuracy of 97%, 98% and 99% respectively. The results of the deep learning models show that the models can assist radiologists for quick, accurate and unbiased diagnosis for Covid-19.