A Comparative study of Lung Cancer Detection and Classification approaches in CT images Preeti Katiyar Department of Electronics and Communication Engineering Guru Gobind Singh Indraprastha University New Delhi, India preetikatiyar27@gmail.com Krishna Singh Department of Electronics and Communication Engineering G.B Pant Govt. Engineering College New Delhi, India singhkrishna5@gmail.com Abstract – Lung disease is a genuine medical problem. In India there are roughly 70,275 individual cases in every year are determined to have lung malignancy. However, early identification and treatment can increase the survival rate. Usually Computed Tomography scan imaging is used in the medical field because of its high clarity and low noise. Only CT scans cannot give proper interpretation to radiologist and the medical practitioner, therefore the Computer Aided Diagnosis system will be extremely useful for radiologists to detect the cancer precisely. Many Computer aided system using image processing and Machine learning has been designed. In this survey various segmentation, feature extraction and classification techniques are considered such as Artificial Neural Network, Convolutional Neural Network, SVM, Gray level co-occurrence matrix, Discrete wavelet transform and many more. We observed that the SVM classifier achieved 96% accuracy, ANN achieved 99% accuracy, CNN achieved 94% accuracy and DNN achieved 97% accuracy. Keywords– SVM, CNN, GLCM, CT Scan I. INTRODUCTION Lung cancer is the second most diagnosed disease among a various kind of cancers which is available in the world. In ladies, bosom malignancy is normal and in men, prostate malignancy is normal. Lung malignancy is one of the most severe cancer, with the little survival rate after the finding. It is the most important reason for malignancy demise in both women and men these days. Generally rate of smoking results in lung cancer which include 75% females and 84% males. Around 10-15% instances happen in individuals who never smoked. Such instances are results of blend of hereditary variables and presentation of gas like radon, different types of contaminated air, active and passive smoking and minerals like asbestos. Early detection of the cancer results in the survival of the patient. So it is necessary to find out the lung cancer in the starting phase. To find the irregularity in an image time factor is very crucial, particularly in different kind of cancers. For example, lung malignancy, bosom growth etc. Subsequently, different image Processing techniques are utilized in different stage of treatment for advancement in image for early identification. There are various approaches which are utilized to analyse the lung nodule such as Computed Tomography, Positron Emission Tomography and X-ray. Most of these approaches are costly and needs lots of processing time. So we require a new computer aided design system to identify the lung cancer in its initial stage. In medical radiology, Computer Aided Design System plays an important role. But most of the system does not fulfil the requirement of radiologist. It was described that by using Computed Tomography scans in early stages for the identification of lung tumor, the survival rate of the patient can reach upto 90%. So we usually use CT Scans for CAD System [1]. This CT image is collected from Cancer Image Archive. It works on cancer analysis and has a large database of medical imaging [2]. Fig 1 shows CT Scan of Lung Cancer. Fig.1. CT Scan image of Lung Cancer In general study the lung cancer diagnosis involves many different technologies. The CAD systems are developed for identification of lung cancer in initial stage. A general Computer Aided Diagnosis set-up consists of several steps in identification of the lung cancer. The following techniques are 1) Pre-processing and segmentation 2) Nodule Detection 3) 2020 7th International Conference on Signal Processing and Integrated Networks (SPIN) 978-1-7281-5475-6/20/$31.00 ©2020 IEEE 135 Authorized licensed use limited to: Univ of Calif Santa Barbara. Downloaded on May 30,2020 at 15:50:48 UTC from IEEE Xplore. Restrictions apply.