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The Utilization of Physics Parameter to Classify Histopathology Types of Invasive Ductal Carcinoma (IDC) and Invasive Lobular Carcinoma (ILC) by using K-Nearest Neighbourhood (KNN) Method

Ngurah Gunawan, Anak AgungSupardi, I WayanPoniman, S.Dharmawan, Bagus G.
International Journal of Electrical and Computer Engineering (IJECE) (Sinta 1)Vol. 0 No. 01 Agustus 2018
DOI10.11591/ijece.v8i4.pp2442-2450

Abstrak

Medical imaging process has evolved since 1996 until now. The forming of Computer Aided Diagnostic (CAD) is very helpful to the radiologists to diagnose breast cancer. KNN method is a method to do classification toward the object based on the learning data which the range is nearest to the object. We analysed two types of cancers IDC dan ILC. 10 parameters were observed in 1-10 pixels distance in 145 IDC dan 7 ILC. We found that the Mean of Hm(yd,d) at 1-5 pixeis the only significant parameters that distingguish IDC and ILC. This parameter at 1-5 pixels should be applied in KNN method. This finding need to be tested in diffrerent areas before it will be applied in cancer diagnostic.

Kata Kunci

computer and informaticsinvasive ductal carcinomainvasive lobular carcinomanearest neighbourhood

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The Utilization of Physics Parameter to Classify Histopathology Types of Invasive Ductal Carcinoma (IDC) and Invasive Lobular Carcinoma (ILC) by using K-Nearest Neighbourhood (KNN) Method | International Journal of Electrical and Computer Engineering (IJECE) | Publiora