An Improved Lung Cancer Prediction System using Image Processing
D Kalyani1, C Raghavendra2, K Rajendra Prasad3
1D Kalyani*, Department of Computer Science Engineering, Institute of Aeronautical Engineering, Dundigal, Hyderabad, India.
2C Raghavendra*, Asst. Professor, Department of Computer Science Engineering, Institute of Aeronautical Engineering, Dundigal, Hyderabad, India.
3Dr. K Rajendra Prasad*, Professor & Head, Department of Computer Science Engineering, Institute of Aeronautical Engineering, Dundigal, Hyderabad, India.
Manuscript received on November 12, 2019. | Revised Manuscript received on November 25, 2019. | Manuscript published on 30 November, 2019. | PP: 5059-5063 | Volume-8 Issue-4, November 2019. | Retrieval Number: D8276118419/2019©BEIESP | DOI: 10.35940/ijrte.D8276.118419
Open Access | Ethics and Policies | Cite | Mendeley | Indexing and Abstracting
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Lung cancer is a disease that causes the cells present in the lungs which divide uncontrollably. This uncontrollable division of cells causes tumours which in turn decrease a person’s respiration. Early identification and diagnosis will help people to seek treatment and recover soon. Tumours are an abnormal mass of tissue that results when cells divide more than they should or do not die when they should. Identifying lung cancer in its early stages is very difficult but knowing about its symptoms is quite easy. Symptoms may be similar to those of respiratory problems or infections and sometimes there may be no symptoms at all. In this work mainly deals with the lung cancer detection using image processing techniques were involving all the intermediate stages such as pre-processing stage, noise removal, processing stage, post-processing stage which finally gives output image after all those stages. Doctors can categorize tumour stage as initial or advanced based on patient CT scan report. The abnormal images are subjected to segmentation (threshold segmentation, watershed transformation) to focus on tumour portion. It mainly deals with image quality and clarity. Gabor filter algorithm plays a vital role for image enhancement in removing noise from an image. The ANN method gives us the best performance as it neglects the background and displays the required portion of an image that we need. This image processing technique is one of the most efficient way of detecting lung cancer.
Keywords: CT SCAN Image, Gabor Filter, Image Segmentation, Threshold Segmentation, Watershed Transformation, Binarization.
Scope of the Article: Aggregation, Integration, and Transformation.