PULMONARY NODULE DETECTION AND CLASSIFICATION IN CT IMAGES OVER EMPLOYING R-CAD SYSTEM
The lung is important organ of human body, the functioning of lung is to exchange exchanges the gas in and out from human body. The lifestyle modernization is badly impacting on human body, one of the leading cancer disease is spreading worldwide nowadays in lung cancer disease. These days the Lung cancer patients are increased widely in the world, the major cause of this disease is smoking and increase of air pollution. Correspondingly the medical fields are upgrading the infrastructure to detect the cancerous nodules in pulmonary region. However, the computer science is providing enormous solution with aid of technological ground. The current trends in Image process and Machine learning techniques are offering enormous solution for medical professionals to identify distinct types of cancer disease. This article is contemplating and exploring by employing Robust Computer Assisted Diagnosis (R-CAD) system, the proposed R-CAD system frameworks shapes the various stages through implementation of the model. The major steps involved in implementing R-CAD system are Pre-processing, Segmentation, Feature Extraction, and Classification. This paper exhibits the enhance segmentation technique to segment the ROI from CT images; secondly the feature extraction technique extracts the learnable feature values through GLCM, GLRM and LBP methods. Lastly the modified CNN model classifies the detected pulmonary nodules are benign or malignant. The performance measurement of proposed model is evaluated through Accuracy, Specificity and Sensitivity, and this R-CAD model is delivering the accuracy of 92.9%.
Lung Cancer, Pre-processing, Feature Extraction, Segmentation, Classification, Image Processing, Machine Learning.