Phased searching with NEAT in a Time-Scaled Framework: Experiments on a computer-aided detection system for lung nodules

摘要

ObjectiveIn the field of computer-aided detection (CAD) systems for lung nodules in computed tomography (CT) scans, many image features are presented and many artificial neural network (ANN) classifiers with various structural topologies are analyzed; frequently, the classifier topologies are selected by trial-and-error experiments. To avoid these trial and error approaches, we present a novel classifier that evolves ANNs using genetic algorithms, called “Phased Searching with NEAT in a Time or Generation-Scaled Framework”, integrating feature selection with the classification task.