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Recent Advances in Computer Science and Communications

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ISSN (Print): 2666-2558
ISSN (Online): 2666-2566

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Research Article

Prediction of Tetralogy of Fallot using Fuzzy Clustering

Author(s): K.R. Kosala Devi* and V. Deepa

Volume 13, Issue 4, 2020

Page: [694 - 705] Pages: 12

DOI: 10.2174/2213275912666190612120344

Price: $65

Abstract

Background: Congenital Heart Disease is one of the abnormalities in your heart's structure. To predict the tetralogy of fallot in a heart is a difficult task. Cluster is the collection of data objects, which are similar to one another within the same group and are different from the objects in the other clusters. To detect the edges, the clustering mechanism improve its accuracy by using segmentation, Colour space conversion of an image implemented in Fuzzy c-Means with Edge and Local Information.

Objective: To predict the tetralogy of fallot in a heart, the clustering mechanism is used. Fuzzy c-Means with Edge and Local Information gives an accuracy to detect the edges of a fallot to identify the congential heart disease in an efficient way.

Methods: One of the finest image clustering methods, called as Fuzzy c-Means with Edge and Local Information which will introduce the weights for a pixel value to increase the edge detection accuracy value. It will identify the pixel value within its local neighbor windows to improve the exactness. For evaluation , the Adjusted rand index metrics used to achieve the accurate measurement.

Results: The cluster metrics Adjusted rand index and jaccard index are used to evaluate the Fuzzy c- Means with Edge and Local Information. It gives an accurate results to identify the edges. By evaluating the clustering technique, the Adjusted Rand index, jaccard index gives the accurate values of 0.2, 0.6363, and 0.8333 compared to other clustering methods.

Conclusion: Tetralogy of fallot accurately identified and gives the better performance to detect the edges. And also it will be useful to identify more defects in various heart diseases in a accurate manner. Fuzzy c-Means with Edge and Local Information and Gray level Co-occurrence matrix are more promising than other Clustering Techniques.

Keywords: Edge detection, Gray scale conversion, Clustering Techniques, GLCM, FELICM, Canny Edge Detection.

Graphical Abstract

[1]
A. Garje, and Y.G. Adhav, "Design and simulation of blocked blood vessel for early detection of heart diseases", In: IEEE International Symposium on Physics and Technology of Sensors, 2015.
[http://dx.doi.org/10.1109/ISPTS.2015.7220113]
[2]
F. Prati, E. Regar, G.S. Mintz, E. Arbustini, C. Di Mario, I-K. Jang, T. Akasaka, M. Costa, G. Guagliumi, E. Grube, Y. Ozaki, F. Pinto, and P.W. Serruys, "Expert review document on methodology, terminology, and clinical applications of optical coherence tomography: Physical principles, methodology of image acquisition, and clinical application for assessment of coronary arteries and atherosclerosis", Eur. Heart J., vol. 31, no. 4, pp. 401-415, 2010.
[http://dx.doi.org/10.1093/eurheartj/ehp433] [PMID: 19892716]
[3]
A.K. Pandey, P. Pandey, and K.L. Jaiswal, "Classification model for the heart disease diagnosis", Global J. Med. Res., vol. 14, pp. 8-14, 2014.
[4]
K. Shrivastava, N. Gupta, and N. Sharma, "Survey paper on image segmentation using K-means clustering", Int. J. Adv. Technol. Eng. Res., vol. 4, no. 5, pp. 8-11, 2014.
[5]
E.P. Thakur, and E.S. Dhiman, "An efficient image segmentation technique by integrating FELICM with negative selection algorithm", Int. J. Sign. Process. Image Process. Pattern Recogn., vol. 8, no. 10, pp. 63-10, 2015.
[http://dx.doi.org/10.14257/ijsip.2015.8.10.07]
[6]
S. Bhowmik, and V. Datta, "A survey on clustering based image segmentation", Int. J. Adv. Res. Comput. Eng. Technol., vol. 1, no. 5, pp. 280-284, 2012.
[7]
M. Xess, and S.A. Agnes, "“Survey on clustering based color image segmentation and novel approaches to FCM algorithm”", Int. J. Res. Eng. Technol., .pp. 346-349, 2013.,
[8]
W. Khan, "Image segmentation techniques: A survey", J. Image Graph., vol. 1, no. 4, pp. 166-170, 2013.
[9]
M.A. Rahman, and M. Islam, ""A hybrid clustering technique combining a novel genetic algorithm with K-means."", Knowledge Base. Syst., vol. 71, pp. 345-365, 2014 .
[10]
P. Tamijeselvy, V. Palanisamy, and T. Purusothaman, "Performance analysis of clustering algorithms in brain tumor detection of MR images", Eur. J. Sci. Res., vol. 62, no. 3, pp. 321-330, 2011.
[11]
M.S. Sonawane, and C.A. Dhawale, ""A brief survey on image segmentation methods."", Int. J. Comput. Appl., vol. 975, p. 8887, 2015 .
[12]
I. Ahmad, I. Moon, and S.J. Shin, "Color-to-grayscale algorithms effect on edge detection - A comparative study", In 2018 International Conference on Electronics, Information, and Communication (ICEIC), 2018
[http://dx.doi.org/10.23919/ELINFOCOM.2018.8330719]
[13]
X. Zheng, Q. Lei, R. Yao, Y. Gong, and Q. Yin, "Image segmentation based on adaptive K-means algorithm", EURASIP J. Image Video Process., vol. 2018, no. 1, p. 68, 2018.
[http://dx.doi.org/10.1186/s13640-018-0309-3]
[14]
R. Shinde, and S. Arjun, "An intelligent heart disease prediction system using K-means clustering and naïve bayes algorithm", Int. J. Comput. Sci. Inf. Technol., vol. 6, no. 1, pp. 637-639, 2015.
[15]
N.J. Gandhi, V.J. Shah Jr. and R. Kshirsagar, “Mean shift technique for image segmentation and modified canny edge detection algorithm for circle detection", In International Conference on Communication and Signal Processing, India, pp. 246-250, 2014,
[http://dx.doi.org/10.1109/ICCSP.2014.6949838]
[16]
V. Bhatnagar, R. Majhi, and P. Jena, "Comparative performance evaluation of clustering algorithms for grouping manufacturing firms", Arab. J. Sci. Eng., vol. 43, pp. 4071-4083, 2018.
[http://dx.doi.org/10.1007/s13369-017-2788-4]
[17]
S. Zhang, and H.S. Wong, "ARImp: A generalized adjusted rand index for cluster ensembles", 20th International Conference on Pattern Recognition, vol. 71, 2010 pp. 778-781
[18]
A.S. Shirkhorshidi, S. Aghabozorgi, and T.Y. Wah, “A comparison study on similarity and dissimilarity measures in clustering continuous data”, PLoS one, vol. E10, no. 12, pp. e0144059, 2015,
[http://dx.doi.org/10.1371/journal.pone.0144059]
[19]
S. Dang, “Performance evaluation of clustering algorithm using different datasets”, J. Inform. Eng. Appl., vol. 5, pp. 167-173, 2015,
[20]
J.M. Santos, and M. Embrechts, “On the use of the adjusted rand index as a metric for evaluating supervised classification", In International Conference on Artificial Neural Networks, vol. 5769, pp. 175-184, 2009.,
[http://dx.doi.org/10.1007/978-3-642-04277-5_18]

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