Mustika Mentari, Yuita Arum Sari, Ratih Kartika Dewi
As the advancement of technology skin cancer detection need to be automated with the use of dermoscopy image. Outlier and overfitting are the problem in feature extraction of dermoscopy image, this can be caused by skin type, uneven cancer distribution or sampling error. This study proposed melanoma skin cancer detection by fuzzy K-Nearest Neighbour (FuzzykNN) with Lp-norm integrated with Linear Discriminant Analysis (LDA) to reduce the problem of outlier and overfitting. Input used in this study are images with RGB channel, then it adapted to RGBr. Dimensional reduction with LDA result in features with highest eigen value. LDA in this research select 2 discriminant, they are tumor area and minimum tumor area in R channel. This features then classified by fuzzykNN with Lp-Norm. Integration of LDA and Lp-norm in classification can reduce the problem of overfitting. This study results in 72% accuracy when the value of p and k are 25. Integration of LDA and fuzzykNN with Lp-norm has better result than unintegrated method. © 2016, Universitas Pesantren Tinggi Darul Ulum. All rights reserved.
Teknologi Informasi, Politeknik Negeri Malang, Malang, Indonesia; Teknik Informatika, Universitas Brawijaya, Malang, Indonesia
Research at a Glance
Register to unlock
Topics & SDG Alignment
Register to unlock
Collaboration
Register to unlock
Author Profile (Selected)
Register to unlock
References Overview
Register to unlock
Journal & Source
Register to unlock
Metadata & Integrity
Register to unlock