Lung Cancer Detection Using Semantic Based ANN Approach
DOI:
https://doi.org/10.26438/ijcse/v6i10.194199Keywords:
Machine Learning Algorithm, Artificial Neural networks, MATLAB, Data Sets, Genetic AlgorithmAbstract
Medical science is an important part segment of our day to day life. It generates medical report and other statistics in the form of multimedia format. The analysis and prediction of related disease can be done using the data analysis. Every prediction and analysis required image processing and its feature extraction. Processing a data with multiple feature aspect is still a challenging issue. Lung is the parts which carry multiple diseases as it’s a major body cycle process. Further which a proper classification and prediction is problem formulation with several research algorithm. Network based processing of data make use of its phases and help out to find more feature analysis and further detailed classification. In this paper an advance algorithm with semantic and ANN model is proposed for the lung cancer disease prediction from its image format. The analysis is compared with traditional SVM approach and proposed Semantic ANN approach. Implementation is performed using the images collected from web medical resources using MATLAB platform. The computed result shows the efficiency of proposed network layer based semantic solution for the lung cancer prediction and its feature analysis.
References
[1] Azian Azamimi Abdullah, Syamimi Mardiah Shaharum, "Lung Cancer Cell Classification Method utilizing Artificial Neural Network", Information Engineering Letters, ISSN:21604114, Volume 2, Issue 1, March 2012.
[2] Mohammad Reza Zare, Ahmed Mueen, Woo Chaw Seng, Mohammad Hamza Awedh, "Correspondence Systems and Networks Combined Feature Extraction on Medical X-beam Images" Third International Conference on Computational Intelligence, Bali, Indonesia, July 26-July 28, 2011.
[3] M. Gomathi, Dr. P. Thangaraj, "A Computer Aided Diagnosis System for Detection of Lung Cancer Nodules Using Extreme Learning Machine", International Journal of Engineering Science And Technology Vol.2(10), 2010.
[4] Jia Tong, Zhao Da-Zhe, Wei Ying, Zhu Xin-Hua, Wang Xu, "PC Aided Lung Nodule Detection Based On CT Images", IEEE/ICME International Conference on Complex Medical Engineering, 2007.
[5] Taher F, Sammouda R, ―Lung Cancer Detection By Using Artificial Neural Network and Fuzzy Clustering Methods‖, IEEE GCC Conference and exhibition(GCC), 2011, 295-298.
[6] Tong J, ZHAO D, Ying W, ZHU X and Xu W, ―Computer-Aided Lung Nodule Detection Based on CT Images ― IEEE-International Conference on Complex Medical Engineering, 2007, 816 – 819.
[7] Tseng L, Huang L, ―An Adaptive Thresholding Method for Automatic Lung Segmentation in CT Images ― IEEE AFRICON, September 23-25, 2009, pp 1-5
[8] Chaudhary A, Singh S, ―Lung Cancer Detection Using Digital Image Processing‖, IJREAS vol 2, Issue 2, 2012, 1351-1359.
[9] Abdullah An and Mohamaddiah H, Development of Cellular Neural Network Algorithm for Detecting Lung Cancer Symptoms, IEEE EMBS Conference on Biomedical Engineering and Sciences, 2010, 138-143.
[10] Suzuki K, Shi Z, and Zhang J, ―Supervised Enhancement of Lung Nodules by utilization of a Massive-Training Artificial Neural Network (MTANN) in Computer-supported diagnosis‖, IEEE, nineteenth International Conference on Pattern Recognition, 2008,1-4.
[11] Shi Z, Suzuki K and He L, ―Reducing FPs in Nodule Detection utilizing Neural Networks Ensemble‖, IEEE, Second International symposium on data science and building, 331-333.
[12] Parveen S and Kavitha C, ―Detection of Lung Cancer Nodules Using Automatic Region Growing Method‖, IEEE-31661 International Conference on Computing and Networking Technologies (ICCCNT, 2013) 1-6.
[13] Tune Y, Cai W, Kim J and Feng D,―A Multistage Discriminative Model for Tumor and Lymph Node Detection in Thoracic Images‖, IEEE Transactions on Medical Imaging, Vol 31, No 5, 2012, 1061-1075.
[14] Ye X, Lin X, Dehmeshki J, Slabaugh G and Beddoe G, ―Shape-Based Computer-Aided Detection of Lung Nodules in Thoracic CT Images‖, IEEE Transactions on Biomedical Engineering, , vol. 56, no. 7, 2009, 1810-1820.
[15] Maini R and Aggarwal H ―A Comprehensive Review of Image Enhancement Techniques‖, Journal of Computing, Vol 2, Issue 3, 2010, 8-13.
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