Desain Perangkat Lunak Berbasis Jaringan Syaraf Tiruan Backpropagation untuk Klasifikasi Citra Rontgen Paru-paru Muhammad Arief Bustomi, Hasan Bisri, Endah Purwanti

Abstract

This paper presents the results of analysis of a software design based backpropagation neural network to classify the X-ray image of the lungs. X-ray image of the lungs should be classified through initial processing prior to discard unneeded information that the image quality can be improved. For the purposes of classification of lung X-ray image into a particular group, use the histogram feature extraction process in X-ray image of the lungs. Once the software system is made, the next important step is two training and testing on the software system. In this study, the design of the software is limited to X-ray image can classify into three groups, namely X-ray image of a normal lung, X-ray image of the lung cancer , and the X-ray image of lungs affected by effusion. The test results show that the performance of software systems that have been created with 500 epoch parameters, error 0001, 0.1 learning rate and the number of neurons in 2500 turned out to have an accuracy rate of 65% .