| Abstrak/Abstract |
Lung cancer is one of the deadliest types of cancer
in the world. Lung cancer detection is necessary to determine the
next steps in dealing with the patients. One of the methods that can
be used for lung cancer detection is a classification method based
on lung cancer image. Most of the models for lung cancer
classification based on lung cancer image are various types of the
neural network model with binarization image pre-processing. As
an image is containing noise, it is needed to remove the noise from
the original image before the binarization process. Wavelet is a
model that can be used to remove the noise from the original
image, i.e. image denoising process. Recurrent Neural Network is
neural network development model which is able to accommodate
the network output to be re-input of the network. The architecture
of Recurrent Neural Network uses Elman network that has
feedback link from the hidden layer to the input layer. The
combination model of Wavelet and Recurrent Neural Network,
called Wavelet Recurrent Neural Network, can be used for lung
cancer classification by applying Wavelet for lung image denoising
process and Recurrent Neural Network for the classification
process. Classification of lung cancer using Wavelet Recurrent
Neural Network provide results with sensitivity, specificity, and
accuracy were respectively 93.75%, 66.67%, and 84% for training
data and 88.24%, 75%, and 84% for testing data. |
