Lungs Nodule Cancer Detection Using Statistical Techniques

Abstract

The detection of lungs nodule cancer by Computer Aided Diagnosis (CAD) system, provide a great support to the medical experts and can lower the death rate. In this research, CAD system is established for early detection of lungs nodule cancer. First, the contrast of an input image is enhanced by applying Contrast Limited Adaptive Histogram Equalization (CLAHE). The Otsu thresholding is applied for segmentation of lungs tumor followed by morphological filters to remove background and other geometrical objects. The resultant image is de-noised by applying Discrete Wavelet Transform (DWT). Gray Level Co-occurrence Matrix (GLCM) is used for features extraction such as correlation, energy, etc. Principle Component Analysis (PCA) is employed for feature selection. Finally, Support Vector Machine (SVM) is used to classify the image into benign (non-cancerous) or malignant (cancerous). The performance parameters such as accuracy, sensitivity, specificity, Peak Signal to Noise Ratio (PSNR), Root Mean Square Errors (RMSE) and Area under the Curve are used for the evaluation of the proposed method. The proposed system is verified on the LIDC dataset. The visual and parametric results of the proposed method are computed and compared with other existing methods.