Abstract
In human beings Lungs are the essential respiratory organs. Their weakness affects respiration and lead to various obstructive lung diseases (OLD) such as bronchitis, asthma or even lung cancer. Predicting OLD at an earlier stage is better than diagnosing and curing them later. If it is determined that a human is prone to OLD, human may remain healthy by doing regular exercise, breathing deeply and essentially quitting smoking. The objective of this work is to develop an automated pre-diagnostic tool as an aid to the doctors. The proposed system does not diagnose, but predict OLD. A 2D Gabor filter and Support Vector Machine (SVM) based iris recognition system has been combined with iridology for the implementation of the proposed system. An eye image database, of 49 people suffering from OLD and 51 healthy people has been created. The overall maximum accuracy of 88.0% with a sample size of 100 is encouraging and reasonably demonstrates the effectiveness of the system.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Stearn N, Swanepoel DW. Identifying hearing loss by means of iridology. African journal of traditional, complimentary and alternate medicines, 2007; 4: 205–10.
Jensen, B. The science and practice of Iridology. California Bernard Jensen Co. 1985; 1.
Jensen B. ‘Iridology charts’, http://www.bernardjensen.com/iridology-charts-c-38_42.html, accessed June 2011.
Daughman J. High confidence visual recognition of persons by a test of statistical independence. IEEE Transaction on Pattern Analysis and Machine Intelligence. 1993; 15: 1148–1161.
Wibawa AD, Purnomo M H. Early detection on the condition of pancreas organ as the cause of diabetes mellitus by real time iris image processing. IEEE Asia Pacific Conference on Circuits and Systems. 2006; 1008–10.
Ma L, Li N. Texture feature extraction and classification for iris diagnosis. International conference on medical biometrics, Lecture notes in computer science, Springer-Verlag. 2007; 168–75.
Lesmana IPD, Purnama IKE, Purnomo MH. Abnormal Condition Detection of Pancreatic Beta-Cells as the Cause of Diabetes Mellitus Based on Iris Image. International Conference on Instrumentation, Communication, Information Technology and Biomedical Engineering. 2011; 150–155.
Kumar A, Anand S. EEG Signal Processing for Monitoring Depth of Anesthesia. IETE Technical Review. 2006; vol 23(3), pp 179–186.
Smith, E, Stein P, Furst J, Raicu DS. Weak Segmentations and Ensemble Learning to Predict Semantic Ratings of Lung Nodules. Machine Learning and Applications (ICMLA), 12th International Conference on. 2013; vol.2, no., pp. 519–524.
Wang J, Valle MD, Goryawala M, Franquiz JM, Mcgoron AJ. Computer-assisted quantification of lung tumors in respiratory gated PET/CT images: phantom study. Medical Biological Engineering and Computing. 2010; 48:49–58.
Pierce R. ‘Spirometry: an essential clinical measurement’ Australian family physician, 2005, vol. 37, no. 7, pp. 535–539.
Lundback B. et al. ‘Not 15 but 50% of smokers develop COPD?-Report from obstructive lung disease Northern Sweden studies”, Respiratory Medicine, 2003, vol. 97, pp. 115–122.
Vestbo J. ‘Definition and Overview’, Global Strategy for the Diagnosis, Management, and Prevention of Chronic Obstructive Pulmonary Disease. Global Initiative for Chronic Obstructive Lung Disease, 2013, pp. 1–7.
Yuh-Chin and Huang T. ‘A clinical guide to occupational and environmental lung diseases’, Humana Press, 2012, pp. 266.
Min J. Y., Min K. B., Cho S. and Paek D. “Combined effect of cigarette smoking and sulphur dioxide on lung functions in Koreans” Journal of Toxicology and Environmental Health, Part-A, 2008, vol. 71, pp. 301–303.
I-SCAN-2 Dual iris Scanner. http://www.crossmatch.com/i-scan-2.php. Accessed 16 July 2011.
Wildes R, Asmuth J, Green G, Hsu S, Kolczynski R, Matey J, McBride S. A system for automated iris recognition. IEEE Workshop on Applications of Computer Vision, Sarasota, FL. 1994; 121–28.
Daughman J. How iris recognition works? IEEE Transaction on Circuits and Systems for Video Technology. 2004; 14: 21–30.
Zhu Yong, Tan Tieniu, Wang Yunhong. Biometric personal identification based on iris patterns. Proceedings of the IEEE international conference on pattern recognition. 2000; 2801–2804.
Cortes C, Vapnik V. Support vector networks-Machine Learning. Kluwer Academic Publishers, Boston. 1995; 273–97.
Kim KA, Chai JY, Yoo TK, Kim SK, Chung K, Kim DW. Mortality prediction of rats in acute hemorrhagic shock using machine-learning techniques. Medical Biological Engineering and Computing. 2013; 51:1059–1067.
Burges CJC. A Tutorial on Support Vector Machines for Pattern Recognition. Kluwer Academic Publishers, Boston. 1998.
Cristianini N, Shawe TD. An Introduction to Support Vector Machines and other kernel-based Learning Methods” Cambridge University press, Cambridge.
Haykin S. Neural Networks-A comprehensive foundation. Pearson Education, 2nd ed. 2004.
Sivasankar K., Sujaritha M., Pasupathi P. and Muthukumar, S. “FCM based iris image analysis for tissue imbalance stage identification”, in Proc. of International Conference on Emerging Trends in Science, Engineering and Technology (INCOSET), 13–14 Dec. 2012, pp. 210–215.
Acknowledgements
The authors would like to acknowledge “GLA University, Mathura” for partially supporting this research. The authors would also like to acknowledge “Dr. Arun Bansal”, “Dr. Poonam Agarwal” and all subjects who helped in developing the database.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Bansal, A., Agarwal, R., Sharma, R.K. (2019). Pre-Diagnostic Tool to Predict Obstructive Lung Diseases Using Iris Recognition System. In: Panigrahi, B., Trivedi, M., Mishra, K., Tiwari, S., Singh, P. (eds) Smart Innovations in Communication and Computational Sciences. Advances in Intelligent Systems and Computing, vol 669. Springer, Singapore. https://doi.org/10.1007/978-981-10-8968-8_7
Download citation
DOI: https://doi.org/10.1007/978-981-10-8968-8_7
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-8967-1
Online ISBN: 978-981-10-8968-8
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)