Implementation of CRNN Method for Lung Cancer Detection based on Microarray Data

Lung Cancer is one of the cancer types with most significant mortality rate, mainly because disease's slow detection. Therefore, early identification this disease crucial. However, primary issue microarray curse dimensionality. This problem related to characteristic data, which has a small sample size yet many attributes. Moreover, could lower accuracy detection systems. Various machines and deep learning techniques have been researched solve problem. paper implemented method named Convolutional Recurrent Neural Network (CRNN) build system. neural networks (CNN) are used extract features, recurrent (RNN) summarize derived features. CNN RNN methods combined in CRNN derive advantages each methods. Several previous research uses system using medical image biomarkers (MRI or CT scan). Thus, researchers concluded that achieved higher than independently. was by microarray-based dataset. Furthermore, different drop-out values compared determine best value for result shows gave RNN. The highest 91%, while 83% 71% accuracy, respectively.