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Breast cancer detection using K ‐nearest neighbors data mining method obtained from the bow‐tie antenna dataset
Author(s) -
Aydın Emine Avşar,
Kaya Keleş Mümine
Publication year - 2017
Publication title -
international journal of rf and microwave computer‐aided engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.335
H-Index - 39
eISSN - 1099-047X
pISSN - 1096-4290
DOI - 10.1002/mmce.21098
Subject(s) - breast cancer , standing wave ratio , computer science , radiation pattern , return loss , permittivity , bandwidth (computing) , microwave imaging , microwave , antenna (radio) , dielectric , cancer , materials science , telecommunications , medicine , microstrip antenna , optoelectronics
Breast cancer, has been a significant cancer type for women on the society. Early diagnosis and timely medical treatment are important key factors spreading to the other tissues and permitting long‐time survival of patients. Since the existing methods have several serious shortfalls, microwave imaging method for the diagnosis of early stage tumors has been interested by different scientific research groups in terms of moderating endogenous the electrical property difference between healthy tissue and malignancies. In this article, both an ultra‐wideband bow‐tie antenna with enhanced bandwidth and a 3D breast model which has different electrical properties which are permittivity and conductivity is created in simulation tool to solve electromagnetic field values. Return loss, VSWR, and radiation pattern characteristics, which are significant antenna parameters, are simulated and obtained whether the antenna possess an efficient characteristic or not. Electric field values over the breast tissue in which there is a tumor or not tumor are evaluated. In this article, above‐mentioned values of frequency bandwidth, dielectric constant of antenna's substrate, electric field, and tumor information were consisted in their dataset. This dataset obtained from the Bow‐Tie Antenna was used to detect the breast cancer with one of the data mining method, which is K‐Nearest Neighbor Algorithm.