Analysis of a feature-deselective neuroevolution classifier (FD-NEAT) in a computer-aided lung nodule detection system for CT images

Systems for Computer-Aided Detection (CAD), specifically for lung nodule detection received increasing attention in recent years. This is in tandem with the observation that patients who are diagnosed with early stage lung cancer and who undergo curative resection have a much better prognosis. In this paper, we analyze the performance of a novel feature-deselective neuroevolution method called FD-NEAT to retain relevant features derived from CT images and evolve neural networks that perform well for combined feature selection and classification. Network performance is analyzed based on radiologists' ratings of various lung nodule characteristics defined in the LIDC database. The analysis shows that the FD-NEAT classifier relates well with the radiologists' perception in almost all the defined nodule characteristics, and shows that FD-NEAT evolves networks that are less complex than the fixed-topology ANN in terms of number of connections.

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