Pulmonary nodule detection on computed tomography using neuro-evolutionary scheme

Pulmonary nodule detection is an image processing approach to detect lung cancer from thoracic computed tomography (CT) images. Lung segmentation is the first step to segment lung regions from CT images. Nodule candidates are detected from the segmented lung regions. Further, nodule classification is performed to identify the true nodules from the false positives. In a recent paper, linear discriminant analysis (LDA) shows better performance than quadratic discriminant analysis for nodule classification. In the proposed method, the performance of the existing LDA method is improved by introducing additional discriminant features extracted by using multiple discriminant analysis. Further, a noble nonlinear classifier is used to overcome the limitation of the linear classifier. For the nonlinear classification, multilayer perceptron is used using a new powerful learning algorithm. The new learning algorithm is a combination of genetic algorithm (GA) and particle swarm optimization. Finally, the performance of the proposed method is compared with the existing LDA and convolutional neural network methods. The new learning algorithm of the proposed method is also compared with backpropagation (BP) and GA optimized BP algorithms. The overall performance of the proposed method is remarkable.