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.