According to statistics, lung cancer is the leading cause of cancer related deaths compared to any other type of cancer in the world. Lung cancer is contributing about 1.3 million deaths per year globally. Further, these reports indicate that the survival rate of lung cancer is only 14 percentages but still, if defective nodules are detected at an early stage, the survival rate can be increased up to 50 percentages. Thus the early detection of lung nodules is important in the treatment of lung cancer. These research papers contribute survey of Lung cancer identification in various aspects.
Han, H. Li, L. Han, F. ;Song, B. Moore, W. Liang, Z.Fast and Adaptive Detection of Pulmonary Nodules in Thoracic CT Images Using a Hierarchical Vector Quantization Scheme IEE Journal of Biomedical and Health Informatics, Vol. 9, Issue 2 , pp. 648 - 659 , 2014.
Temesguen Messay, Russell C. Hardie, Steven K. Rogers, ‘A new computationally efficient CAD system for pulmonary nodule detection in CT imagery’, Medical Image Analysis 14 390–406, 2010
Wook-Jin Choi, Tae-Sun Choi, ‘Genetic programming-based feature transform and classification for the automatic detection of pulmonary nodules on computed tomography images’, Information Sciences 212 57–78, 2012.
Shanhui Sun, Christian Bauer, and Reinhard Beichel, ‘Automated 3-D Segmentation of Lungs With Lung Cancer in CT Data Using a Novel Robust Active Shape Model Approach’, IEEE transactions on medical imaging, vol. 31, no. 2.
Zakaria suliman zubi, Rema Asheibani Saad, Using Some Data Mining Techniques for Early Diagnosis of Lung Cancer, Recent Researches in Artificial Intelligence, Knowledge Engineering and Data Bases, pp-32-37, 2014
Tao Xu, Mrindal Mandal, Richard Long, Irene Cheng and Anup Basu, ‘An edge-region force guided active shape approach for automatic lung field detection in chest radiographs’, Computerized Medical Imaging and Graphics, 2012.
Eva M. van Rikxoort, Mathias Prokop, Bartjan de Hoop, Max A. Viergever, Josien P. W. Pluim, and Bram van Ginneken, ‘Automatic Segmentation of Pulmonary Lobes Robust Against Incomplete Fissures’, IEEE transactions on medical imaging, vol. 29, no. 6, 2010.
Boroczky, L, Luyin Zhao, Lee, K.P, “Feature Subset Selection for Improving the Performance of False Positive Reduction in Lung Nodule CAD”, IEEE Transaction on Information Technology in Biomedicine, Vol.10, issue 2, 2006.
P.Ramachandran, N.Girija, T.Bhuvaneswari, Early Detection and Prevention of Cancer using Data Mining Techniques, International Journal of Computer Applications, Vol. 97, Issue 13, pp. 7622-7626, 2014
Monali Dey, Siddharth Swarup Rautaray, Study and Analysis of Data mining Algorithms for Healthcare Decision Support System, International Journal of Computer Science and Information Technologies, Vol. 5 , issue 1 , pp. 470-477, 2014.
Juliet R Rajan1, Jefrin J Prakash2, Early Diagnosis of Lung Cancer using a Mining Tool, International Journal of Emerging Trends in Computer Science, Special issue, 2013
Zhenqiu Liu, Dechang Chen, Guoliang Tian, Man-Lai Tang, Ming Tan, and Li Sheng, Chapter 2, Efficient Support Vector Machine Method for Survival Prediction with SEER Data, University of Maryland at Baltimore, Advances in Computational Biology, pp-11-18, 2010.
Mining Lung Cancer Data for Smokers and Non- Smokers by Using Data Mining Techniques, International Journal of Advanced Research in Computer and Communication Engineering, Vol. 3, Issue 7, 2014
Xujiong Ye, Xinyu Lin, Jamshid Dehmeshki, Greg Slabaugh, Gareth Beddoe ‘Shape-Based Computer-Aided Detection of Lung Nodules in Thoracic CT Images’ , IEEE transactions on biomedical engineering, vol. 56, 2009.
Ming-Tai Wu1 Jain-Shing Wu1 Chung-Nan Lee, Ming-Cheng Chen1, A Genetic Algorithm-Fuzzy-Based Voting Mechanism Combined with Hadoop Map-Reduce Technique for Microarray Data Classification, pp. 41-48, 2013
Data Mining, Image mining, Image Retrieval, Image classification and Lung cancer detection
