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Computer vision techniques always had played a salient role in numerous medical fields, especially in image diagnosis. Amidst a global pandemic situation, one of the archetypal methods assisting healthcare professionals in diagnosing various types of lung cancers, heart diseases, and COVID-19 infection is the Computed Tomography (CT) medical imaging technique. Segmentation of Lung and Infection with high accuracy in COVID-19 CT scans can play a vital role in the prognosis and diagnosis of a mass population of infected patients. Most of the existing works are predominately based on large private data sets that are practically impossible to obtain during a pandemic situation. Moreover, it is difficult to compare the segmentation methods as the data set are obtained in various geographical areas and developed and implemented in different environments. To help the current global pandemic situation, we are proposing a highly data-efficient method that gets trained on 20 expert annotated COVID-19 cases. To increase the efficiency rate further, the proposed model has been implemented on NVIDIA - Jetson Nano (System-on-Chip) to completely exploit the GPU performance for a medical application machine learning module. To compare the results, we tested the performance with conventional U-Net architecture and calculated the performance metrics. The proposed state-of-art method proves better than the conventional architecture delivering a Dice Similarity Coefficient of 99%.