Lung Nodule Classification Using Deep Features in Ct Images
This paper suggest a novel arrangement technique for the four varieties of lung nodules, i.e. well circumscribed, vascularized, juxta pleural, and pleural tail, in minute dose computed tomography scans. The proposed technique is built on contextual analysis by merging the lung nodule and adjacent anatomical structures, and has three main stages: an adaptive patch based division is used to build concentric multilevel partition; then, a new feature set is planned to incorporate intensity, texture, and gradient information for image patch feature description, and then a contextual latent semantic analysis based classifier is designed to compute the probabilistic estimations for the related images. Our proposed method was estimated on a publicly existing dataset and obviously demonstrated promising classification performance.