Cancer is a sickness brought about by an uncontrolled division of eccentric cells in any part of human body. It is in the top of few places in the killer disease list and pervades in the entire world, but still on the rise. Most of the cases an early detection of lung cancer is cumbersome. This research paper is aimed to present an effective and an efficient way of computer-assisted detection method for lung cancer. In this research we used a set of lung computed tomography scanned images as inputs, obtained from lung image archives and applied image processing techniques such as feature extraction, segmentation. In this approach, a proper combination of Adaptive thresholding segmentation algorithm has been used for segmenting input images, a well-known Support Vector Machine image classification algorithm has been used for lung tumor classification and Content-based image retrieval technique has been used to compare lung image features such as contract, intensity, texture and shape. A set of patient personal data is included to get more accurate and correct prediction results, and it is dealt with data mining approach. The proposed segmentation method shows improved prediction results.

癌症是由于人体任何部位的离心细胞不受控制的分裂所引起的疾病。在杀手疾病列表中，它位居前几位，并在全世界流行，但仍在上升。在大多数情况下，早期发现肺癌很麻烦。这篇研究论文的目的是提出一种有效且有效的计算机辅助肺癌检测方法。在这项研究中，我们使用了一组肺部计算机断层扫描扫描图像作为输入，这些图像是从肺部图像档案库中获得的，并应用了图像处理技术，例如特征提取，分割。在这种方法中，自适应阈值分割算法的正确组合已用于分割输入图像，众所周知的支持向量机图像分类算法已用于肺肿瘤分类，基于内容的图像检索技术已用于比较肺部图像特征，例如收缩，强度，纹理和形状。包括一组患者个人数据，以获取更准确和正确的预测结果，并使用数据挖掘方法进行处理。所提出的分割方法显示出改进的预测结果。