Lung Cancer Detection and Classification using CT Scan Image Processing

Lung cancer is the stereotypical cancer after breast cancer in this era. The survival rate in this cancer is less than other cancers as well. Lung cancer screening can help find cancer at an early stage. If the disease is found and treated at an early stage, the chances of recovery are more. Computed Tomography (CT) is the most preferred and effective way of lung cancer screening. However, visual interpretation of CT scan images is quite difficult, time consuming and may lead to wrong interpretation of the malignancy. Therefore, computer aided techniques are required for proper and accurate detection of the lung diseases. There are several techniques available in the literature. In this paper, we propose a novel approach of lung cancer detection and classification by image processing of the CT scan. We apply different preprocessing techniques for smoothing and image enhancement. Then we apply thresholding and edge detection for segmentation of the region of interest (ROI) of the lung tumor. Finally, we compute several geometrical features of the extracted ROI and classify them into severity levels as Benign and Malignant using support vector machine (SVM) classifier. We find significant accuracy in detection of lung cancer nodules and estimation of the severity level using our proposed method.