IJMTES – A SURVEY ON EARLY DIAGNOSIS AND FORECASTING OF LUNG CANCER

Journal Title : International Journal of Modern Trends in Engineering and Science

Author’s Name : Annie Steffi Sydney | S Binilsundar  unnamed

Volume 03 Issue 03 2016

ISSN no:  2348-3121

Page no: 28-30

Abstract – Lung cancer, also said as  lung carcinoma , the earlier diagnosis of lung cancer is challenging task .  The signs of lung cancer are coughing , weight loss, shortness of breath, and chest pains. The Maximum causes of  lung cancer are tobacco smoking.  Lung cancer may be detected by  chest radiographs and computed tomography (CT) scans.  For early diagnosis and forecasting of  image processing technique are widely used for lung cancer, genetic as well as environmental problems are very significant in preventing lung cancer. In clinical trails of  cancer tumours such as lung cancer the time factor is needed for screening the tumours  in target images. Forecasting of lung cancer by decision tree algorithm. In this proposed system pre-processing of images and feature extraction processes and neural network classifier to Analyze the state of patient whether it is normal or abnormal. If the lung cancer is successfully diagnosed and forecasted in its earlier stages . The early diagnosis and Forecasting of lung cancer should play a emerging role in the detection process and also improve the safety of patient.

Keywords— Chest radiographs; Computed Tomography scans; Decision Tree Algorithm 

Reference

  1. T. Sowmiya, M. Gopi, M. New Begin L.ThomasRobinson “Optimization of Lung Cancer using Modern data mining techniques.” International Journal of Engineering Research ISSN:2319- 6890)(online),2347-5013(print)VolumeNo.3,Issue No.5, pp : 309-3149(2014)
  2. Ada, Rajneet Kaur “Early Detection and Prediction of Lung Cancer Survival using Neural Network Classifier”, (IJAIEM)Volume 2, Issue 6, June 2013
  3. Dasu Vaman Ravi Prasad,“Lung cancer detection using image processing techniques”, International journal of latest trends in engineering and technology.(2013)
  4. S Vishukumar K. Patela and Pavan Shrivastavab, “Lung A Cancer Classification Using Image Processing”, International Journal of Engineering and Innovative Technology Volume 2, Issue 3, September 2012
  5. Fatma Taher1,*, Naoufel Werghi1, Hussain Al-Ahmad1, Rachid Sammouda2, “Lung Cancer Detection Using Artificial Neural Network and Fuzzy Clustering Methods,” American Journal of Biomedical Engineering 2012, 2(3): 136-142
  6. Morphological Operators, CS/BIOEN 4640: “Image Processing Basics”, February 23, 2012.
  7. Almas Pathan, Bairu.K.saptalkar, “Detection and Classification of Lung Cancer Using Artificial Neural Network”, International Journal on Advanced Computer Engineering and Communication Technology Vol-1 Issue :2011.
  8. American Cancer Society, “Cancer facts & figures2010” http://www.cancer.org/acs/groups/content/@epidemiologysurveilance/documents/document/acspc- 026238.pdf (2010).
  9. “Multileve-l Thresholding Based on Histogram Difference,” in 17th International Conference on Systems, Signals and Image Processing. 2010.
  10. Nunes, É.d.O. and M.G. Pérez., Nunes, É.d.O. and M.G. Pérez., “Medical Image Segmentation by Multilevel Thresholding Based on Histogram Difference,” in17th International Conference on Systems, Signals and Image Processing. 2010.
  11. S.Shah, “Automatic Cell Images segmentation using a Shape-Classification Model”, Proceedings of IAPR Conference on Machine vision Applications, pp. 428-432,Tokyo, Japan,(2007)

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