Lightweight Model For The Prediction of COVID-19 Through The Detection And Segmentation of Lesions in Chest CT Scans
We introduce a lightweight Mask R-CNN model that segments areas with the Ground Glass Opacity and Consolidation
in chest CT scans. The model uses truncated ResNet18 and ResNet34 nets with a single layer of Feature Pyramid Network
as a backbone net, thus substantially reducing the number of the parameters and the training time compared to similar
solutions using deeper networks...
Without any data balancing and manipulations, and using only a small fraction of
the training data, COVID-CT-Mask-Net classification model with 6.12M total and 600K trainable parameters derived
from Mask R-CNN, achieves 91.35% COVID-19 sensitivity, 91.63% Common