Image processing- based Lung Tumor-Detection and Classification using 3D Micro-Calcification of CT Images
AbstractLung cancer is deadly of all tumor disease with a high death rate as the identification of lung disease at the postponed stage is risky. Hence, the early prediction of lung disease and its treatment is vital to increase the recovery rate. The pattern recognition model based on the micro-calcification of lung CT images aids to classify the lung lesion disease using its texture and statistical features. The features selected are coefficient of reflection and density of mass for the binned lung CT image physical feature measurement that aids in identifying the malignant nodule. Then, the thresholding method is applied with the three-dimensional (3D) sectional Region of Interest (ROI) using the material dimensions. Thus, the lung lump dimension with its physical and statistical features are analyzed using 100 suspected images with ten normal images. This model includes an SVM classifier for the classification of normal and cancer images, exhibiting 98% of accuracy for the proposed system.
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