TY  - JOUR
T1  - COVID-19 Segmentation and Classification from CT Scan Images
AU - Ramachandra Rathod, Sridevi AU - Kaur Khanuja, Harmeet 
JO  - Asian Journal of Information Technology
VL  - 20
IS  - 6
SP  - 168
EP  - 173
PY  - 2021
DA  - 2001/08/19
SN  - 1682-3915
DO  - ajit.2021.168.173
UR  - https://makhillpublications.co/view-article.php?doi=ajit.2021.168.173
KW  - COVID-19
KW  -segmentation
KW  -k-means
KW  -convolutional neural network
KW  -pneumonia
AB  - The pandemic coronavirus disease-2019
(COVID-19) has infected millions of people in over 200
countries and territories as of 2021. It is very necessary to
detect COVID-19 in the initial stage to provide
appropriate medical treatment to patients and also to
protect the uninfected people. For this reason, we develop
a framework to automatically segment COVID-19 CT
images using k-means clustering and use them to train
proposed convolutional neural network to classify
COVID-19 from normal CT images. Rapid growth in
machine learning and deep learning has been doing great
work to reduce time of radiologists by assisting them in
the diagnosis of COVID-19. Our framework is evaluated
upon 349 positive and 397 negative CT scans to detect
COVID-19 and help in taking appropriate diagnostic
decisions. To evaluate the performance of proposed
approach, we compared our results with pre-trained
models such as VGG19, InceptionV3 and ResNet50.
ER  -