Detection of lung nodules in CT images using random forest classifier
M Mary Adline Priya, Dr. S Joseph Jawhar
The classification and identification of the disease in medical images were helpful in biomedical applications. Lung nodule detection becomes the crucial part in the lung cancer diagnosis. The accurate segmentation of lung nodules from computerized tomography scans is important for lung cancer diagnosis and research. The main aim of this work is to propose a novel Computer-aided detection (CAD) system based on a Contextual clustering combined with region growing for assisting radiologists in early identification of lung cancer from computed tomography (CT) scans. Instead of using conventional thresholding approach, this proposed work uses Contextual Clustering which yields a more accurate segmentation of the lungs from the chest volume. Following segmentation GLCM and LBP features are extracted which are then classified using three different classifiers namely Random forest, SVM and k-NN.
M Mary Adline Priya, Dr. S Joseph Jawhar. Detection of lung nodules in CT images using random forest classifier. International Journal of Advanced Engineering and Technology, Volume 3, Issue 3, 2019, Pages 72-76