A novel approach for detection of coronavirus disease from computed tomography scan images using the pivot distribution count method

ABSTRACT

Detecting a person infected with coronavirus disease is a challenging task. In some cases, the infection is asymptomatic in nature. A computed tomography scan of images provides information about lungs and helps detect coronavirus disease infected lungs. An accurate algorithm helps the specialist to detect corona virus-infected lungs easily. In this article, a new technique called the pivot distribution count method has been proposed to extract the texture features of computed tomography scanned images of lungs and apply it for the detection and analysis of coronavirus disease. The technique is compared with a recently developed methodology called pixel range calculation method and some state-of-art methods like local binary pattern and gliding box method. The ‘severe acute respiratory syndrome coronavirus-2 computed tomography scan dataset’ was used for our experiments. The experimental results show that the pivot distribution count method produces better accuracy for detecting the infection of coronavirus disease with less computational time. It is also observed that the detection accuracy obtained from coronavirus disease infected images is 98% and from non-infected images is 94% using the pivot distribution count method, which is much higher as compared to other methods.

KEYWORDS:

• Texture classification
• coronavirus
• pivot distribution count
• pixel range calculation

Acknowledgments

The authors acknowledge the support given by the Veer Surendra Sai University of Technology, under TEQIP-III, Government of India.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Notes on contributors

Abadhan Ranganath

Abadhan Ranganath received his Bachelor of Computer Application degree in the year 2010 and Masters of Computer Application degree in the year 2012 from Utkal University. He has completed his master’s in Information Technology from Biju Patnaik University of Technology, India, in the year 2014. He is currently pursuing his PhD in the Department of Information Technology at Veer Surendra Sai University of Technology, India. His research interest includes computer vision, image processing and image analysis.

Pradip Kumar Sahu

Dr. Pradip Kumar Sahu received the Ph.D. degree in Electronics and Electrical Communication Engineering from Indian Institute of Technology Kharagpur, India. He is currently working as an Associate Professor in the Department of Information Technology, Veer Surendra Sai University of Technology, Burla. He has published many papers in reputed international journals and conferences. He is also a reviewer for many reputed international journals and conferences. His area of research includes embedded systems, VLSI, network-on-chip, system-on-chip, optimization techniques, Evolutionary computations, cloud computing, information retrieval and data mining.

Manas Ranjan Senapati

Dr. Manas Ranjan Senapati received his Ph.D. from the Dept. of CSE from Biju Patnaik University of Technology. Currently, he is working as an Associate Professor in the Department of Information Technology, Veer Surendra Sai University of Technology, Burla. He has published more than 50 articles in reputed international journals and conferences. He is also a reviewer for many reputed international journals and conferences. His area of research includes soft computing, evolutionary techniques, information retrieval, data mining, big data analysis, pattern analysis, clustering and image processing.