The application of Wavelet Recurrent Neural Network for lung cancer classification

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.