International Journal of Medical Imaging 2017; 5(5): 58-62 http://www.sciencepublishinggroup.com/j/ijmi doi: 10.11648/j.ijmi.20170505.12 ISSN: 2330-8303 (Print); ISSN: 2330-832X (Online) Detection of Lung Cancer Using Digital Image Processing Techniques: A Comparative Study Munimanda Prem Chander 1 , M. Venkateshwara Rao 2 , T. V. Rajinikanth 3 1 Department of Computer Science and Engineering GIT, GITAM UNIVERSITY, Visakhapatnam, India 2 Department of Information Technology, GIT, GITAM UNIVERSITY, Visakhapatnam, India 3 Department of Computer Science and Engineering, Srinidhi Institute of Science and Technology, Hyderabad, India Email address: premchandermunimanda@gmail.com (M. P. Chander), mandapati_venkat@yahoo.com (M. V. Rao), rajinitv@gmail.com (T. V. Rajinikanth) To cite this article: Munimanda Prem Chander, M. Venkateshwara Rao, T. V. Rajinikanth. Detection of Lung Cancer Using Digital Image Processing Techniques: A Comparative Study. International Journal of Medical Imaging. Vol. 5, No. 5, 2017, pp. 58-62. doi: 10.11648/j.ijmi.20170505.12 Received: April 16, 2017; Accepted: May 8, 2017; Published: December 9, 2017 Abstract: This paper focuses on early stage lung cancer detection. Lung cancer is prominent cancer as it states large number of deaths of more than a million every year. It creates need of detecting the lung nodule at early stage in Computer Tomography medical images. So to detect the occurrence of cancer nodule at early stage, the requirement of methods and techniques is increasing. There are different methods and techniques existing but none of them provide a better accuracy of detection. One of the techniques is content based image retrieval Computer Aided Diagnosis System (CAD) for early detection of lung nodules from the Chest Computer Tomography (CT) images. This optimization algorithm allows physicians to identify the nodules present in the CT lung images in the early stage hence the lung cancer. The MATLAB image processing toolbox based implementation is done on the CT lung images and the classifications of these images are carried out. The performance measures like the classification rate and the false positive rates are analyzed. Keywords: Classification, Lung Cancer Detection, Accuracy, Image Processing Techniques 1. Introduction In nature, lung disease plays a major role in health issue. In any form of lung disease mainly the breathing gets affected, here are some common forms of lung diseases. Acute bronchitis, asthma, Chronic Obstructive Pulmonary Disease (COPD), Acute Respiratory Distress Syndrome (ARDS) and Lung cancer. This annual report provides the estimated numbers of new cancer cases and deaths in 2015, as well as current cancer incidence, mortality, and survival statistics and information on cancer symptoms, risk factors, early detection, and treatment. In 2015, there will be an estimated 1,658,370 new cancer cases diagnosed and 589,430 cancer deaths in the US. As per World cancer report 2016 lung cancer is the most common cause of cancer-related death in men and women, The major causes of the lung diseases are smoking, inhaling the drugs, smoke and allergic materials. The computed tomography (CT) images assists in detecting the extreme of the lung diseases. For the analysis of the proposed method CT image is sufficient also the visibility of soft tissue is better. There are several types of lung cancer, and these are divided into two main groups: Small cell lung cancer and non-small cell lung cancer which has three subtypes: Carcinoma, Adeno carcinoma and Squamous cell carcinomas [1]. Proper lung cancer staging is essential for optimizing therapy and assessing prognosis. A cancer’s stage is based on the size or extent of the primary tumor and whether it has spread to nearby lymph nodes or other areas of the body. A number of different staging systems are used to classify cancer. Lung cancer is the second most commonly diagnosed cancer in both men and women. An estimated 224,390 new cases of lung cancer are expected in 2016, accounting for about 14% of all cancer diagnoses. The mentioned CAD schemes were not applied directly to the domain of lung cancer screening, yet they provide theoretical justification and potential to be effective tools to improve CT lung cancer screening. One has to examine carefully the reported results