Authors:
Veena Mayya
1
;
2
;
Karthik K.
2
;
Sowmya S. Kamath
2
;
Krishnananda Karadka
3
and
Jayakumar Jeganathan
4
Affiliations:
1
Department of Information & Communication Technology, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka, India
;
2
Healthcare Analytics and Language Engineering (HALE) Lab, Dept. of Information Technology, National Institute of Technology Karnataka, Surathkal, Mangalore 575025, India
;
3
Penzigo Technology Solutions Pvt. Ltd., NITK-Science and Technology Entrepreneurs’ Park (STEP), NITK Surathkal, India
;
4
Dept. of Medicine, Kasturba Medical College, Manipal Academy of Higher Education (MAHE), Karnataka, India
Keyword(s):
Computational and Artificial Intelligence, Decision Support Systems, Automated Diagnosis, COVID-19.
Abstract:
The COVID-19 pandemic has affected the world on a global scale, infecting nearly 68 million people across
the world, with over 1.5 million fatalities as of December 2020. A cost-effective early-screening strategy is
crucial to prevent new outbreaks and to curtail the rapid spread. Chest X-ray images have been widely used
to diagnose various lung conditions such as pneumonia, emphysema, broken ribs and cancer. In this work,
we explore the utility of chest X-ray images and available expert-written diagnosis reports, for training neural
network models to learn disease representations for diagnosis of COVID-19. A manually curated dataset
consisting of 450 chest X-rays of COVID-19 patients and 2,000 non-COVID cases, along with their diagnosis
reports were collected from reputed online sources. Convolutional neural network models were trained on
this multimodal dataset, for prediction of COVID-19 induced pneumonia. A comprehensive clinical decision
support system powered by ensemb
le deep learning models (CADNN) is designed and deployed on the weba.
The system also provides a relevance feedback mechanism through which it learns multimodal COVID-19
representations for supporting clinical decisions.
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