Classification of Lung Cancer Stages from CT Scan Images Using Image Processing and k-Nearest Neighbours

Lung cancer is the prevalent cause of death among people around the world. The detection of existence of lung cancer can be performed in a variety of ways, such as magnetic resonance imaging (MRI), radiography, and computed tomography (CT). Such techniques take up a lot of time and financial resources. Nevertheless, for the detection of lung cancer, CT provides a lower cost, fast imaging time and increased availability. Early diagnosis of lung cancer may help physicians treat patients in order to minimize the number of deaths. This paper revolves around the categorization of lung Cancer Stages from CT Scan Images Using Image Processing and k-Nearest Neighbours. The central objective of this study is therefore to establish an image processing technique for extracting features of lung cancer from CT scan images. Extracting the features from the segmented image can help to detect the cancer inside the lung. The purposed method comprises the following steps by using image processing techniques: data collection, data pre-processing, features selection and lung cancer classification. The pre-processing was done using a median filter to remove noise contained in the images. There are three features that need to be extracted which are; area, perimeter and centroid. Finally, the set of data with these features were used as inputs for lung cancer classification. By analysis results, kNN method has a high accuracy of 98.15%.