The ongoing pandemic of COVID-19 has shown the limitations of our current medical institutions. There is a need for research in the field of automated diagnosis for speeding up the process while maintaining accuracy and reducing computational requirements. In this work, an automatic diagnosis of COVID-19 infection from CT scans of the patients using Deep Learning technique is proposed. The proposed model, ReCOV-101 uses full chest CT scans to detect varying degrees of COVID-19 infection, and requires less computational power. Moreover, in order to improve the detection accuracy the CT-scans were preprocessed by employing segmentation and interpolation. The proposed scheme is based on the residual network, taking advantage of skip connection, allowing the model to go deeper. Moreover, the model was trained on a single enterpriselevel GPU such that it can easily be provided on the edge of the network, reducing communication with the cloud often required for processing the data. The objective of this work is to demonstrate a less hardware-intensive approach for COVID-19 detection with excellent performance that can be combined with medical equipment and help ease the examination procedure. Moreover, with the proposed model an accuracy of 94.9% was achieved.