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COVID-19 has been detrimental in terms of the number of fatalities and risingnumber of critical patients across the world. According to the UNDP (UnitedNational Development Programme) Socio-Economic programme, aimed at the COVID-19crisis, the pandemic is far more than a health crisis: it is affectingsocieties and economies at their core. There has been greater developmentsrecently in the chest X-ray-based imaging technique as part of the COVID-19diagnosis especially using Convolution Neural Networks (CNN) for recognisingand classifying images. However, given the limitation of supervised labelledimaging data, the classification and predictive risk modelling of medicaldiagnosis tend to compromise. This paper aims to identify and monitor theeffects of COVID-19 on the human lungs by employing Deep Neural Networks onaxial CT (Chest Computed Tomography) scan of lungs. We have adopted Mask RCNN,with ResNet50 and ResNet101 as its backbone, to segment the regions, affectedby COVID-19 coronavirus. Using the regions of human lungs, where symptoms havemanifested, the model classifies condition of the patient as either "Mild" or"Alarming". Moreover, the model is deployed on the Google Cloud Platform (GCP)to simulate the online usage of the model for performance evaluation andaccuracy improvement. The ResNet101 backbone model produces an F1 score of 0.85and faster prediction scores with an average time of 9.04 seconds perinference.

Comparative performance analysis of the ResNet backbones of Mask RCNN to segment the signs of COVID-19 in chest CT scans