• SWATI SENDRE
• SHAILENDRA KUMAR SINGH

In previous years, image processing had covered a large area over biomedical applications in diagnosis of several of diseases in medical images. Lung Cancer detection and classification is one of the most widely used applications by many researchers. In this review paper we treat a very important research subject that affects directly the Lungs of human body Lung cancer detection and classification techniques verify the presence the cancer in Lung and check the probability of cancer. This study of this paper is aims to highlight the significance of data analytics and machine learning in prognosis in health sciences, particularly in detecting life threatening and terminal diseases like cancer. Here, we consider lung cancer for our study. For this purpose, preexisting lung cancer patient’s data are collected to get the desired results. This paper presents the better Computer Aided Diagnosing (CAD) system for automatic detection of lung cancer. The initial process is lung region detection by applying basic image processing techniques such as Bit-Plane Slicing, Erosion, Median Filter, Dilation, Outlining, Lung Border Extraction and Flood-Fill algorithms to the CT scan images. After the lung region is detected, the segmentation is carried out with the help of Mean Shift clustering algorithm. With these, the features are extracted and the diagnosis rules are generated. These rules are then used for learning with the help of Random Forest.

Lung cancer detection, CT scan images, Mean Shift clustering algorithm, Computer Aided Diagnosing, Lung Border Extraction.

" A Review on an Innovative Approach for Lung Cancer Detection and Classification from Medical Images", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.5, Issue 6, page no.109-112, June-2018, Available :http://www.jetir.org/papers/JETIR1806262.pdf

" A Review on an Innovative Approach for Lung Cancer Detection and Classification from Medical Images", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.5, Issue 6, page no. pp109-112, June-2018, Available at : http://www.jetir.org/papers/JETIR1806262.pdf