Abstract
A hybridized diagnosis system called Neuro-Fuzzy Inference System (NFIS) uses neural network for the classification of lung nodule into benign or malignant and then a fuzzy logic for detecting various stages of lung cancer is proposed in this paper. Using fuzzy logic based algorithms such as Enhanced Fuzzy C-Means (EFCM) and Enhanced Fuzzy Possibilistic C-Means (EFPCM), the required features from the lung CT scan image are segmented and extracted using GLCM and GLDM matrix. Then the features are selected using DRASS algorithm. These features are fed as input for Radial Basis Function Neural Network (RBFNN) classifier with k-means learning algorithm for detecting the lung cancer. Once the lung cancerous nodules are detected, the result of RBFNN is combined with fuzzy inference system that determines appropriate stage of the lung cancer. Experiments have been conducted on ILD lung image datasets with 104 cases. Results reveal that our proposed NFIS effectively classify lung nodule into benign or malignant along with the appropriate stage with considerable improvement in respect of Recall/Sensitivity 94.44%, Specificity 92%, Precision 96.22%, Accuracy 93.67%, and False Positive Rate 0.08.
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Acknowledgment
We acknowledge the support extended by VGST, Govt. of Karnataka, in sponsoring this research work vide ref. No. KSTePS/VGST/GRD-684/KFIST(L1)/2018. Dated 27/08/2018.
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Rangaswamy, C., Raju, G.T., Seshikala, G. (2020). Neuro-Fuzzy Inference Approach for Detecting Stages of Lung Cancer. In: Smys, S., Tavares, J., Balas, V., Iliyasu, A. (eds) Computational Vision and Bio-Inspired Computing. ICCVBIC 2019. Advances in Intelligent Systems and Computing, vol 1108. Springer, Cham. https://doi.org/10.1007/978-3-030-37218-7_51
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DOI: https://doi.org/10.1007/978-3-030-37218-7_51
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