• 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 review paper we already described various techniques and algorithm. 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.

" An Implementation of Lung Cancer Detection and Classification System from Medical Images using Matlab", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.5, Issue 7, page no.167-175, July-2018, Available :http://www.jetir.org/papers/JETIR1807492.pdf

" An Implementation of Lung Cancer Detection and Classification System from Medical Images using Matlab", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.5, Issue 7, page no. pp167-175, July-2018, Available at : http://www.jetir.org/papers/JETIR1807492.pdf