• Nachiket Kelkar
• Niraj Mate
• Atharv Kukade
• Abhijit Kulkarni
• Pradnya Mehta

Lung cancer is one of the most prominent and deleterious forms of cancer and affects about 234,030 people every year on an average. The odds of a man developing lung cancer in his lifetime are 1 in 15. On a positive note, Lung Cancer death rates have significantly declined over the past decade due to early detection and treatment. Computed Tomography (CT) can be more efficient than X-ray. Hence, the proposed system uses CT images for detection of lung cancer. The proposed system contains several steps like image acquisition, pre-processing, binarization, thresholding, segmentation, feature extraction and detection of the presence and the stage of cancer if it is present. In first stage, Binarization technique is used to convert CT image to binary image and then compare it with threshold value to detect lung cancer. In second stage, segmentation is performed to segment the lung CT image and a strong feature extraction method has been introduced to extract some important features of segmented images like area, perimeter and roundness of Cancer cells. Extracted features are given to the system to detect the presence and hence the stage of existing cancer. The system then goes ahead and generates a report which is sent to the doctor directly for further analysis.

Machine Learning, Image Processing, Preprocessing, Binarization, Segmentation and Feature Extraction

"Lung Cancer Detection & Recommendation of Oncologist using Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.5, Issue 12, page no.243-247, December-2018, Available :http://www.jetir.org/papers/JETIR1812837.pdf

"Lung Cancer Detection & Recommendation of Oncologist using Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.5, Issue 12, page no. pp243-247, December-2018, Available at : http://www.jetir.org/papers/JETIR1812837.pdf