@article{MAKHILLAJIT20212066842,
    title = {COVID-19 Segmentation and Classification from CT Scan Images},
    journal = {Asian Journal of Information Technology},
    volume = {20},
    number = {6},
    pages = {168-173},
    year = {2021},
    issn = {1682-3915},
    doi = {ajit.2021.168.173},
    url = {https://makhillpublications.co/view-article.php?issn=1682-3915&doi=ajit.2021.168.173},
    author = {Sridevi and},
    keywords = {COVID-19,segmentation,k-means,convolutional neural network,pneumonia},
    abstract = {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.}
    }