TY  - JOUR
ID  - city27223
UR  - https://researchlakejournals.com/index.php/AAIML/article/view/51
IS  - 1
A1  - Ter-Sarkisov, A.
N2  - We introduce a lightweight model that segments areas with the Ground Glass Opacity and Consolidation and predicts COVID-19 from chest CT scans. The model uses truncated ResNet18 and ResNet34 as a backbone net, and Mask R-CNN functionality for lesion segmentation. Without any class balancing and data 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, achieves 91.35% COVID-19 sensitivity, 91.63% Common Pneumonia sensitivity, 96.98% true negative rate and 93.95% overall accuracy on COVIDx-CT dataset (21191 images). The full source code, models and pre-trained weights are available on https://github.com/AlexTS1980/COVID-CT-Mask-Net.
VL  - 2
TI  - Lightweight Model for the Prediction of COVID-19 Through the Detection and Segmentation of Lesions in Chest CT Scans
AV  - public
EP  - 15
N1  - © 2021 by the authors; licensee Research Lake International Inc., Canada. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution License.
Y1  - 2021/05/31/
PB  - ResearchLake
JF  - International Journal of Automation, Artificial Intelligence and Machine Learning
KW  - Convolutional neural networks
KW  -  COVID-19
KW  -  Lesion segmentation
KW  -  Lesion detection
SN  - 2563-7568
SP  - 1
ER  -