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Comparison of Multi Layered Percepton and Radial Basis Function Classification Performance of Lung Cancer Data
Yessi Jusman, Zul Indra, Roni Salambue, Siti Nurul Aqmariah Mohd Kanafiah

Department of Electrical Engineering, Faculty of Engineering, Universitas Muhammadiyah Yogyakarta, Tamantirto, Kasihan, Bantul, Daerah Istimewa Yogyakarta 55183, Indonesia

Department of Informatics Engineering, Faculty of Engineering, Universitas Abdurrab, Pekanbaru, 28291 Riau, Indonesia

Department of Information System, Faculty of Math and Science, Universitas Riau, Pekanbaru, 28293 Riau, Indonesia

School of Mechatronic Engineering, Universiti Malaysia Perlis, Pauh Putra Campus, 02600 Arau, Perlis, Malaysia


Abstract

Lung cancer was the most commonly diagnosed cancer as well as the leading cause of cancer death in males in 2008 globally. The way used to detect lung cancer are through examination chest X-ray, Computed Tomography (CT) scan, and Magnetic Resonance Imaging results. The accurate and efisien analysis of the imaging results are important to ensure the minimal time processing. A computed assisted diagnosis system is the crusial research which can conduct the analysis efficiently and efectively. This paper aimed to compare the classification performances of Multi Layered Perceptron (MLP) and Radial Basis Function (RBF) techniques. The public lung cancer datasets was used as training and testing data in the classfication techniques. Ten fold cross validation was used for dividing data before classifying techniques. The accuracy performances are compared to check a better technique for classification step.

Keywords: Lung cancer, datasets, multi layered perceptron, radial basis function, classification.

Topic: International Symposium of Engineering, Technology, and Health Sciences

Link: https://ifory.id/abstract/Ke4Uyuxvf7YP

Conference: The 3rd International Conference on Sustainability and Innovation (ICoSI 2019)

Plain Format | Corresponding Author (Yessi Jusman)

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