A Model: Lung Nodule Detection and Classification by SVM Network
European Journal of Molecular & Clinical Medicine,
2020, Volume 7, Issue 8, Pages 3228-3238
AbstractIn this work, we suggest a unique system for pulmonary Nodule Awareness, Segmentation, and Identification of Nodules in CT images. This process consists of 4 phases: Phase-1: Segmentation of lung location by ACM (Active Contour Modeling). Phase-2: Apply of mask strategies. Phase-3: Shift from non-separated nodules to separated nodules. Phase-4: SVM (Support Vector Machine) classifier identifies nodules with the help of 2D hypothetical and 3D anatomical characteristics. Shape of nodules extracted by ACM. Therefore, all cavity and solid nodule segmentation accuracy is high. Moreover, lung tissues divided into 4 groups: a. Lung Wall, b. Parenchyma, c. Bronchioles, d. Nodule. Accordingly, this system implemented in MATLAB Software and accuracy checked with different efficient methods with publicly available data sets (LIDC, LUNA16).
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