Lung Nodule Detection System

The Existing approach consist of CAD scheme with Virtual Dual Energy. CXRs where ribs and clavicles are suppressed with massive-training artificial neural networks (MTANNs) To reduce rib-induced FPs and detect nodules overlapping with ribs, we incorporated the VDE technology in our CADe scheme. The VDE technology suppressed rib and clavicle opacities in CXRs while maintaining soft-tissue opacity by use of the MTANN technique that had been trained with real dual-energy imaging. A nonlinear support vector classifier was employed for classification of the nodule candidates. The use of VDE technology, the sensitivity and specificity of the CADe scheme for detection of nodules and especially subtle nodules. The proposed approach concentrates on detecting nodules, early stages of cancer diseases, appearing n patients lung. Most of the nodules can be observed after carefully selection of parameters. This scheme shows an efficient lung nodule segmentation through thresholding and watershed segmentation. The proposed system also uses the VDE technology suppressed rib and clavicle opacities in CXRs while maintaining soft-tissue opacity by use of the MTANN technique that had been trained with real dual-energy imaging, But instead of support vector machine the classifier used here is Artificial neural network i.e Gradient Descent and Back propogation algorithms. This scheme also gives the stages of the cancer based on the features extracted of the nodules .So depending on the stages the radiologist or the physician can identify and give proper treatment.