BREAST CANCER CLASSIFICATION: A DEEP LEARNING BASED OUTLIER DETECTION INCORPORATING EGRET SWARM OPTIMIZATION ALGORITHM
Keywords:
BC, WBC, WDBS, DL, MLAbstract
Breast cancer (BC) continues to pose a major global health challenge, characterized by elevated incidence and mortality rates. Timely detection and precise classification are essential for successful treatment. Conventional BC therapies encompass surgery, radiation, and pharmacological interventions aimed at eradicating microscopic tumors. Recent advancements in machine learning (ML) and deep learning (DL) have demonstrated potential in improving the accuracy of BC diagnosis and classification. This study employed an innovative accurate classification model for BC utilizing a Deep Neural Network-Convolutional neural network Egret Swarm Optimization framework. The methodology comprises three primary stages: data pre-processing through Enhanced Linear Discriminant Analysis (ELDA), outlier identification via a Deep Neural Network (DNN), Ultimately, Convolutional Neural Network with Egret Swarm Optimization (CNN-ESO) algorithm categorizes the BC data as either benign or malignant. The efficacy of this method was confirmed using the Wisconsin Breast Cancer (WBC) and Wisconsin Diagnosis Breast Cancer (WDBC) datasets.
Downloads
References
[1]. Mridha, M. F., Hamid, M. A., Monowar, M. M., Keya, A. J., Ohi, A. Q., Islam, M. R., & Kim, J. M. (2021). A comprehensive survey on deep-learning-based breast cancer diagnosis. Cancers, 13(23), 6116.
[2]. Mao, N., Yin, P., Wang, Q., Liu, M., Dong, J., Zhang, X., ... & Hong, N. (2019). Added value of radiomics on mammography for breast cancer diagnosis: a feasibility study. JACR, 16(4), 485-491.
[3]. Wang, H., Feng, J., Bu, Q., Liu, F., Zhang, M., Ren, Y., & Lv, Y. (2018). Breast mass detection in digital mammogram based on gestalt psychology. J. Healthc. Eng., 2018
[4]. Valvano, G., Santini, G., Martini, N., Ripoli, A., Iacconi, C., Chiappino, D., & Della Latta, D. (2019). Convolutional neural networks for the segmentation of microcalcification in mammography imaging. J. Healthc. Eng., 2019.
[5]. Shaban, W. M., Rabie, A. H., Saleh, A. I., & Abo-Elsoud, M. A. (2020). A new COVID-19 Patients Detection Strategy (CPDS) based on hybrid feature selection and enhanced KNN classifier. Knowledge-Based Systems, 205, 106270.
[6]. Haq, A. U., Zeb, A., Lei, Z., & Zhang, D. (2021). Forecasting daily stock trend using multi filter feature selection and deep learning. Expert Systems with Applications, 168, 114444.
[7]. Nguyen, B. H., Xue, B., & Zhang, M. (2020). A survey on swarm intelligence approaches to feature selection in data mining. Swarm and Evolutionary Computation, 54, 100663.
[8]. Rabie, A. H., Ali, S. H., Saleh, A. I., & Ali, H. A. (2020). A new outlier rejection methodology for supporting load forecasting in smart grids based on big data. Cluster Computing, 23, 509-535.
[9]. Amarasingh, K., Kenney, K., & Manic, M. (2018, July). Toward explainable deep neural network-based anomaly detection. In 2018 11th international conference on human system interaction (HSI) (pp. 311-317). IEEE.
[10]. Munir, M., Siddiqui, S. A., Dengel, A., & Ahmed, S. (2018). DeepAnT: A deep learning approach for unsupervised anomaly detection in time series. Ieee Access, 7, 1991-2005.
[11]. Gao, J., Song, X., Wen, Q., Wang, P., Sun, L., & Xu, H. (2020). Robusttad: Robust time series anomaly detection via decomposition and convolutional neural networks. arXiv preprint arXiv:2002.09545.
[12]. Gómez-Flores, W., & Hernández-López, J. (2020). Assessment of the invariance and discriminant power of morphological features under geometric transformations for breast tumor classification. Computer methods and programs in biomedicine, 185, 105173.
[13]. Lahoura, V., Singh, H., Aggarwal, A., Sharma, B., Mohammed, M. A., Damaševičius, R., ... & Cengiz, K. (2021). Cloud computing-based framework for breast cancer diagnosis using extreme learning machine. Diagnostics, 11(2), 241.
[14]. Liu, Y., Ren, L., Cao, X., & Tong, Y. (2020). Breast tumors recognition based on edge feature extraction using support vector machine. Biomedical Signal Processing and Control, 58, 101825.
[15]. Irfan, R., Almazroi, A.A., Rauf, H.T., Damaševičius, R., Nasr, E.A. and Abdelgawad, A.E., (2021). Dilated semantic segmentation for breast ultrasonic lesion detection using parallel feature fusion. Diagnostics, 11(7), p.1212.
[16]. Adebiyi, M. O., Arowolo, M. O., Mshelia, M. D., & Olugbara, O. O. (2022). A linear discriminant analysis and classification model for breast cancer diagnosis. Applied Sciences, 12(22), 11455.
[17]. Sun, W., Tseng, T. L. B., Zhang, J., & Qian, W. (2017). Enhancing deep convolutional neural network scheme for breast cancer diagnosis with unlabeled data. Computerized Medical Imaging and Graphics, 57, 4-9.
[18]. Adege, A. B., Lin, H. P., Tarekegn, G. B., & Jeng, S. S. (2018). Applying deep neural network (DNN) for robust indoor localization in multi-building environment. Applied Sciences, 8(7), 1062.
[19]. Chen, Z., Francis, A., Li, S., Liao, B., Xiao, D., Ha, T. T., ... & Cao, X. (2022). Egret swarm optimization algorithm: an evolutionary computation approach for model free optimization. Biomimetics, 7(4), 144.
[20]. Goni, M. O. F., Hasnain, F. M. S., Siddique, M. A. I., Jyoti, O., & Rahaman, M. H. (2020, December). Breast cancer detection using deep neural network. In 2020 23rd International Conference on Computer and Information Technology (ICCIT) (pp. 1-5). IEEE.
[21]. Yusuf, A. B., Dima, R. M., & Aina, S. K. (2021). Optimized breast cancer classification using feature selection and outliers detection. JNSPS, 298-307.
[22]. J. Schmidhuber, “Deep Learning in Neural Networks: An Overview,” Neural Networks, vol. 61, pp. 85–117, Jan. 2015. https://doi.org/10.1016/j.neunet.2014.09.003
Downloads
Published
Issue
Section
License
Copyright (c) 2025 S. Maria Sylviaa, Dr. N. Sudha (Author)
This work is licensed under a Creative Commons Attribution 4.0 International License.
Article published in Academic Research Journal of Science and Technology (ARJST) is an open access under the Creative Commons Attribution 4.0 International License -BY, (http://creativecommons.org/licenses/by/4.0/), which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
