A Novel Fuzzy Fusion-Based Segmentation with Enhanced Feature Selection for Improved Breast Cancer Detection
Keywords:
AdaBoost, AlexNet, DeepLab v3+, ESO, Level Set Method, MSER, MSLBPAbstract
This paper presents a hybrid methodology for breast cancer detection by
leveraging fuzzy fusion-based segmentation, advanced feature extraction, and ensemble learning. The approach employs a novel fuzzy fusion-based segmentation framework was proposed, combining three segmentation techniques—threshold-based segmentation using Rényi Entropy, region-based segmentation using the Level Set Method, and semantic segmentation with DeepLab v3+. This fusion approach effectively captured both fine grained details and larger pathological regions. Features extracted from these segmented regions included Multi-Scale Local Binary Patterns (MSLBP), Maximally Stable Extremal Regions (MSER), and AlexNet-based deep features. To optimize feature selection, the Enhanced Snake Optimization (ESO) algorithm is applied, selecting the most relevant features while reducing redundancy. For classification, the Ensemble AdaBoost Classifier is used, combining multiple weak classifiers to improve performance by focusing on difficult instances. Experimental results show that the proposed hybrid methodology outperforms traditional approaches, offering superior classification accuracy, sensitivity, and specificity. The method demonstrates the effectiveness of combining advanced feature extraction and ensemble learning for reliable and accurate breast cancer detection.
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References
Bayrak, E.A., Kırcı, P. and Ensari, T., 2019, April. Comparison of machine learning methods for breast cancer diagnosis. In 2019 Scientific meeting on electrical-electronics & biomedical engineering and computer science (EBBT) (pp. 1-3). Ieee.
Dhahri, H., Al Maghayreh, E., Mahmood, A., Elkilani, W. and Faisal Nagi, M., 2019. Automated breast cancer diagnosis based on machine learning algorithms. Journal of healthcare engineering, 2019(1), p.4253641.
Ganggayah, M.D., Taib, N.A., Har, Y.C., Lio, P. and Dhillon, S.K., 2019. Predicting factors for survival of breast cancer patients using machine learning techniques. BMC medical informatics and decision making, 19, pp.1-17.
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