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Predicting COVID-19 Pneumonia Severity based on Chest X-ray with Deep Learning

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2021
Swati Shekapure, Nikita Pagar, Bhagyashree Kulkarni, Dinesh Choudhary, Priti Parkhad

Swati Shekapure, Nikita Pagar, Bhagyashree Kulkarni, Dinesh Choudhary and Priti Parkhad. Predicting COVID-19 Pneumonia Severity based on Chest X-ray with Deep Learning. International Journal of Computer Applications 183(7):9-11, June 2021. BibTeX

	author = {Swati Shekapure and Nikita Pagar and Bhagyashree Kulkarni and Dinesh Choudhary and Priti Parkhad},
	title = {Predicting COVID-19 Pneumonia Severity based on Chest X-ray with Deep Learning},
	journal = {International Journal of Computer Applications},
	issue_date = {June 2021},
	volume = {183},
	number = {7},
	month = {Jun},
	year = {2021},
	issn = {0975-8887},
	pages = {9-11},
	numpages = {3},
	url = {},
	doi = {10.5120/ijca2021921353},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}


Pneumonia is an infectious disease that affects one or both lungs in the human body commonly caused by bacteria called Streptococcus pneumonia. It is an infection of microscopic particles in the air sacs of the lungs, called alveoli. Chest X-Rays are used to diagnose pneumonia and which needs an expert radiotherapist for evaluation. This may vary over time from practitioner to practitioner. This is based upon the person’s experience too. Therefore, an automated system is required that can help patients to diagnose pneumonia without any of these constraints. We propose an image-based automated system that detects pneumonia diseases using Artificial intelligence. The system will be making the use of computational techniques for analyzing, processing, and classifying the image data predicated upon various features of the images. Unwanted noise is filtered and the resulting image is processed for enhancing the image. Complex techniques are used for feature extraction like the Convolutional Neural Network (CNN) followed by classifying images based upon various algorithms. The diagnosis report is generated as an output that also contains a severity score. This system will generate more precise results and will provide them faster than the traditional method, making this application more efficient and dependable. This application can also be used as a real-time teaching tool for medical students in the radiology domain.


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Image Processing, Artificial Intelligence(AI), Neural Network, Deep Learning, COVID-19, Viral pneumonia.