Abstract
Breast cancer death rates are higher due to the low accessibility of early detection technologies. From the medical point of view, mammography diagnostic technology increases are essential in the detection process. This research work proposes a segmentation for each image by using improved Fuzzy Local Information C-Means (FLICM) algorithms and classification by using the novel local linear wavelet neural network (LLWNN-SCA) model. Further, the weights of the LLWNN model is optimized by using the modified Sine Cosine Algorithm (SCA) to improve the performance of the LLWNN algorithm. By applying an improved FLICM algorithm, the segmented images have undergone the process of feature extraction. The statistical features are extracted from the segmented images and fed as input to the SCA based LLWNN model. The improved FLICM segmentation achieves an accuracy of about 99.25%. Classifiers such as Pattern Recognition Neural Network (PRNN), Feed Forward Neural Network (FFWNN), and Generalized Regression Neural Network (GRNN) are also utilized for classification, and comparison results are presented with the proposed SCA-LLWNN model.
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Mishra, S., Gopi Krishna, T., Kalla, H., Ellappan, V., Aseffa, D.T., Ayane, T.H. (2021). Breast Cancer Detection and Classification Using Improved FLICM Segmentation and Modified SCA Based LLWNN Model. In: Smys, S., Tavares, J.M.R.S., Bestak, R., Shi, F. (eds) Computational Vision and Bio-Inspired Computing. Advances in Intelligent Systems and Computing, vol 1318. Springer, Singapore. https://doi.org/10.1007/978-981-33-6862-0_33
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DOI: https://doi.org/10.1007/978-981-33-6862-0_33
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