Abstract
The Fuzzy Inference System (FIS) plays a vital role in the medicalfield to provide medical assistance to the radiologist to diagnose the abnormality in the medical images. This paper presents a scheme to improve the efficiency of the lung cancer diagnosis system by proposing the segmentation of the suspected lung nodules by region based segmentation and cancer identification by FIS. The proposed method is implemented in two phases. The first phase carries pre-processing for primary noise removal by wiener filter followed by region growing to segment the suspected lung nodules from CT lung images. The second phase carries the classification of the segmented nodules as either benign (normal) or malignant (cancerous) by extracting the features like diameter, shape and intensity values and given as the input to the FIS. The Fuzzy system finds the severity of the suspected lung nodules based on IF-THEN rules. The sensitivity of the proposed system is 92.3%, which show that the proposed work can help the radiologists to increase their diagnostic confidence.
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References
Asbestos cancer resource, http://www.asbestos.net/lung-cancer/lung-cancer-facts
Ganesan, N., Venkatesh, K., Rama, M.A.: Application of Neural Networks in diagnosing cancer diseases using Demographic data. International Journal of Computer Applications 1, 76–82 (2010)
Wu, Y., Wang, N., Hongshezhang: Application of Artificial Neural Networks in the Diagnosis of Lung Cancer by Computed Tomography. In: Sixth International Conference on Natural Computation, China, pp. 147–153 (2010)
Ahmad, F., Mat-Isa, N.A.: Genetic algorithm-Artificial Neural network Hybird intelligence for cancer diagnosis. In: Second international Conference on Computational Intelligence, Communication Systems and Networks (2010)
Clifford Samuel, C., Saravanan, V., Vimala Devi, M.R.: Lung nodule diagnosis from CT images using fuzzy logic. In: International Conference on Computational Intelligence and Multimedia Applications (2007)
Saleem Durai, M.A., Iyengar, N.C.S.N.: Effective analysis and diagnosis of lung cancer using Fuzzy rules. International Journal of Engineering Science and Technology 2(6), 2102–2108 (2010)
Suzuki, K., Shairaishi, J.: False-Positive reduction in computer-aided diagnostic scheme for detecting in chest radiographs by means of massive training artificial neural network. IEEE Transaction on Medical Imaging 24, 1138–1143 (2005)
El-Baz, A., Falk, R.: Promising results for early diagnosis of lung cancer. IEEE transactions on Medical imaging, 1151–1154 (2008)
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© 2011 Springer-Verlag Berlin Heidelberg
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Manikandan, T., Bharathi, N. (2011). Lung Cancer Diagnosis from CT Images Using Fuzzy Inference System. In: Das, V.V., Thankachan, N. (eds) Computational Intelligence and Information Technology. CIIT 2011. Communications in Computer and Information Science, vol 250. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25734-6_110
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DOI: https://doi.org/10.1007/978-3-642-25734-6_110
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-25733-9
Online ISBN: 978-3-642-25734-6
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