Analysis of Thyroid Disease Using K Means and Fuzzy C Means Algorithm
International Journal of Computer Science and Engineering |
© 2019 by SSRG - IJCSE Journal |
Volume 6 Issue 10 |
Year of Publication : 2019 |
Authors : Kirubha.M, Prinitha.R, P.Preethika, A.Samyuktha |
How to Cite?
Kirubha.M, Prinitha.R, P.Preethika, A.Samyuktha, "Analysis of Thyroid Disease Using K Means and Fuzzy C Means Algorithm," SSRG International Journal of Computer Science and Engineering , vol. 6, no. 10, pp. 1-6, 2019. Crossref, https://doi.org/10.14445/23488387/IJCSE-V6I10P101
Abstract:
Thyroid disease is now a days most common and the second largest in the field of endocrine. Classification of this disease is primary problem in clinical treatment. Various research studies estimates that about 42 million people in India suffer from thyroid disease. There are a number of possible thyroid diseases and disorders right from simple goiter to thyroiditis and thyroid cancer. This paper is all about classification of thyroid disease into normal and abnormal. Medical imaging system has done lots of research on thyroid segmentation. The effects of the thyroid disease may be uncomfortable but if they are diagnose in a proper way they can be managed and well treated. Sometimes the disease will be a simple goiter and hence it can be cured naturally there is no need of any treatment but sometimes it might lead to cancer which requires removal of the thyroid gland. The defected thyroid gland can be either chemically removed or surgically removed. The diagnosis methods consist of four stages and they are pre-processing of input images where the image is converted into grey scale for better performance, feature selection, feature extraction and feature classification. Feature classification is based on fuzzy c means clustering.
Keywords:
Thyroid Disease, Data Mining, Fuzzy C Means Clustering
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