Lung Ct Screening With 3D Convolutional Neural Network Architecture

Lung cancer is the most prevalent cancer in the world and early detection and diagnosis enable more treatment options and a far greater chance of survival. Computer-aided detection systems can be used to assist specialists providing a second opinion of the analysis in the detection of pulmonary nodules. Thus, we propose an algorithm based on 3D Convolutional Neural Network to classify pulmonary nodules as benign or malignant from Computed Tomography images. The proposed architecture has two blocks of convolutional layers followed by a pooling layer, two fully connected layers and a softmax layer that represents the network output. The results show an accuracy of 91.60% and an error of 0.2761 in the test set. These are promising results for the application of 3D CNN in the detection of malignant nodules.