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

M. F. Abdullah, S. N. Sulaiman, M. K. Usman, Norimah A. Karim, I. Shuaib, Muhamad Daniyal Irfan Alhamdu, A. I. Che Ani
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Abstract

Lung cancer is the prevalent cause of death among people around the world. The detection of the 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 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 Neighbor. 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 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. Three features 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, the kNN method has a high accuracy of 98.15%.
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基于图像处理和k近邻的CT扫描图像肺癌分期分类
肺癌是世界各地人们死亡的主要原因。肺癌的存在可以通过多种方式进行检测,如磁共振成像(MRI)、放射照相和计算机断层扫描(CT)。这些技术占用了大量的时间和财力。然而,对于肺癌的检测,CT提供了更低的成本,更快的成像时间,并增加了可用性。肺癌的早期诊断可以帮助医生治疗患者,以减少死亡人数。本文围绕CT扫描图像中肺癌分期的分类,采用图像处理和k近邻方法。因此,本研究的中心目标是建立一种从CT扫描图像中提取肺癌特征的图像处理技术。从分割后的图像中提取特征有助于检测肺内肿瘤。本发明利用图像处理技术,包括以下步骤:数据采集、数据预处理、特征选择、肺癌分类。预处理使用中值滤波器去除图像中包含的噪声。需要提取三个特征:面积、周长和质心。最后,将具有这些特征的数据集作为肺癌分类的输入。分析结果表明,kNN方法的准确率高达98.15%。
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