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Knockoff filter‐based feature selection for discrimination of non‐small cell lung cancer in CT image

Lung cancer is one of the deadliest diseases worldwide and the classification of different types of lung cancers in computed tomography (CT) images is also one of the most significant issues in computer-aided diagnosis. It remains a tough task since various image features could be extracted from one single image while part of the features is irrelevant to the final diagnosis results. In this study, a knockoff filter-based approach is proposed to produce the optimal feature set and to minimise the irrelevancy of the output features for the classification of lung cancer in CT images. The proposed feature selection strategy not only can generate the optimal feature subset but also constrain the false discovery rate of the irrelevant features under a specified parameter setting. Ten-fold leave-one-out cross-validation and the area under the receiver operating characteristic curve are both adopted in the experiments to evaluate the performance of the proposed method. The areas under curve of 0.86 ± 0.02 is achieved when the support vector machine classifier is trained on the features determined by the proposed feature selection strategy. The experimental results demonstrate that the presented approach is potentially valuable for lung cancer diagnosis.