In order to diagnose breast cancer, methods such as mammography, MRI, thermal mammography and detection with a simple breast health test device (Brest Angel) are used. Different image processing methods are among the effective methods for detecting different types of tumors in women's breasts. In this article, two types of artificial neural networks are used. 5 statistical features extracted from thermographic images of women's breasts were used to diagnose cancer in neural networks. In this article, back propagation neural network (network 1) is used with Lunberg-Markudat training method and its results are compared with hybrid back propagation-wavelet network (network 2) to investigate the condition of women's breasts. The outputs of the two neural networks used in the article have 2 nodes, which indicate whether the person in question has breast cancer or not with the information given to the neural networks. In network (1), correlation coefficient (R=0.9831) and root mean square error (RMSE=0.5538) were obtained as the best function for network training. In contrast to the network correlation coefficient (2), R=0.9945 and root mean square error (RMSE=0.4665) was obtained. The training time of neural network 1 was 45.51 seconds and network 2 was 33.68 seconds. The results of the designed wavelet-back propagation hybrid neural network show that the proposed network is effective in detecting breast cancer with 99.5% accuracy and is able to detect the health status of women's breasts.
[1] D. Yamada, S. Ohde, Y. Kajiura, K.Yagishita, F. Nozak, K. Suzuki, et al. "Relationship Between Breast Density, Breast Cancer Subtypes, and Prognosis", Clinical Breast Cancer, Vol. 22, No.6, 2022, pp.560-566.
[2] N. Uddin, X. Wang, "Identification of Breast Cancer Subtypes Based on Gene Expression Profiles in Breast Cancer Stroma", Clinical Breast Cancer, Vol.22, No.6, 2022, pp.521-537.
[4] G. Saad, A. Khadour, Q. Kanafani, "ANN and Adaboost Application for Automatic Detection of Microcalcifications in Breast Cancer", The Egyptian Journal of Radiology and Nuclear Medicine, Vol. 47, No.4, 2016, pp.1803-1814.
[5] A. Buciński, T. Bączek, K. Jerzy, R. Szoszkiewicz, J. Załuski, "Clinical Data Analysis Using Artificial Neural Networks (ANN) and Principal Component Analysis (PCA) of Patients with Breast Cancer after Mastectomy", Reports of Practical Oncology & Radiotherapy, Vol.12, No. 1, 2007, pp.9-17.
[6] R. Jafari-Marandi, S. Davarzani, M. Soltanpour Gharibdousti, B.K. Smith , "An Optimum ANN-Based Breast Cancer Diagnosis: Bridging Gaps Between ANN Learning and Decision-Making Goals", Applied Soft Computing, Vol.72, 2018, pp.108-120.
[8] K. Srikanth, UI. Zahoor, S. Huq, AP. Siva Kumar, "Big Data Based Analytic Model to Predict and Classify Breast Cancer Using Improved Fractional Rough Fuzzy K-Means Clustering and Labeled Ensemble Classifier Algorithm", Concurr Com-Pract E, Vol.34, No.10, 2022, pp.e6715.
]10[ پانیذ تیموری و مهدی مزینانی ، راحیل حسینی، "ارائه یک مدل هوشمند قطعه‌بندی مبتنی بر منطق فازی و تبدیل موجک گسسته در تصاویر دیجیتالی جهت شناسایی سرطان معده" نشریه مدل‌سازی در مهندسی، دوره 18، شماره 63، زمستان 1399، صفحه 131-150.
]11[ امین رضایی پناه و علی مبارکی، سعید بحرانی، "بهینه‌سازی شبکه عصبی MLP با استفاده از الگوریتم ژنتیک موازی Fin Grain برای تشخیص سرطان سینه" نشریه مدل‌سازی در مهندسی، دوره 17، شماره 57، تیر1399، صفحه 173-186.
]12[ طاها کشاورز،"ارائه مدل بهینه‌سازی دو هدفه برای برنامه‌ریزی درمان سلول‌های سرطانی به روش پرتو درمانی با حجم تطبیق شده" نشریه مدل‌سازی در مهندسی، دوره 19، شماره 64، اردیبهشت 1400، صفحه 95-107.
[14] JM. Jerez-Aragones, JA. Gomez-Ruiz, G. Ramos-Jimenez, J. Muñoz-Pérez, E. Alba-Conejo, "A Combined Neural Network and Decision Trees Model for Prognosis of Breast Cancer Relapse", Artificial Intelligence in Medicine,Vol. 27, 2003, pp.45–63.
[15] HA. Abbass,"An Evolutionary Artificial Neural Networks Approach for Breast Cancer Diagnosis", Artificial Intelligence in Medicine, Vol.25, 2002, pp. 265–81.
[16] L. Mariani, D. Coradini, E. Biganzoli, P. Boracchi, "Prognostic Factors for Metachronus Contralateral Breast Cancer: A Comparison of the Linear Cox Regression Model Its Artificial Neural Network Extension", Breast Cancer Research and Treatment, Vol.44, No.2, 1997, pp.167–78.
[17] B. Widrow, MA. Lher, "30 Years of Adaptive Neural Networks: Perceptron, Madaline and Back Propagation", Proceedings of the IEEE ,Vol.78, 1990, pp.1415–1442.
[18] E. Biganzoli, P. Boracchi, D. Coradini, M. Grazia Daidone, E. Marubini," Prognosis in Node-Negative Primary Breast Cancer: A Neural Network Analysis of Risk Profiles Using Routinely as Sessed Factors", Annals of Oncology, Vol.14, 2003, pp.1484–93.
[19] A. Belayneh, J. Adamowski, B. Khalil, J. Quilty, "Coupling Machine Learning Methods with Wavelet Transforms and the Bootstrap and Boosting Ensemble Approaches for Drought Prediction", Atmospheric Research, Vol.172-173, 2016, pp.37-47.
[20] A. Aghajani, R. Kazemzadeh, A. Ebrahimi, "A Novel Hybrid Approach for Predicting Wind Farm Power Production Based on Wavelet Transform, Hybrid Neural Networks and Imperialist Competitive Algorithm", Energy Conversion and Management, Vol.121, 2016, pp.232-240.
[21] EM. Golafshani, A. Behnood, M. Arashpour, "Predicting the Compressive Strength of Normal and High-Performance Concretes Using Ann and Anfis Hybridized with Grey Wolf Optimizer", Construction and Building Materials ,Vol.232, 2020,pp.117266.
[24] J. Zupan, J. Gasteiger, Neural Networks for Chemists: An Introduction,1th ed.VCH, Weinheim, 1993.
[25] MT. Hagan, MB. Menhaj," Training feedforward networks with the Marquardt algorithm", IEEE T Neural Network , Vol.5, 1994, pp.989–993.
[26] SS. Patel, AP. Chourasia, SK. Panigrahi, J. Parashar, N. Parvez, M. Kumar," Damage Identification of RC Structures Using Wavelet Transformation", Procedia Engineering, Vol.114, 2016, pp.336-342.
[27] D. Sharma, R. Kumar, A. Jain, "Breast Cancer Prediction Based on Neural Networks and Extra Tree Classifier Using Feature Ensemble Learning", Vol.24, 2022, 100560.
Heidari, M. (2023). Comparison of back propagation neural network with hybrid back propagation-wavelet network for breast cancer diagnosis, based on statistical features extracted from thermographic images of women's breasts.. Journal of Modeling in Engineering, 21(74), 51-64. doi: 10.22075/jme.2023.29162.2368
MLA
Mohammad Heidari. "Comparison of back propagation neural network with hybrid back propagation-wavelet network for breast cancer diagnosis, based on statistical features extracted from thermographic images of women's breasts.", Journal of Modeling in Engineering, 21, 74, 2023, 51-64. doi: 10.22075/jme.2023.29162.2368
HARVARD
Heidari, M. (2023). 'Comparison of back propagation neural network with hybrid back propagation-wavelet network for breast cancer diagnosis, based on statistical features extracted from thermographic images of women's breasts.', Journal of Modeling in Engineering, 21(74), pp. 51-64. doi: 10.22075/jme.2023.29162.2368
VANCOUVER
Heidari, M. Comparison of back propagation neural network with hybrid back propagation-wavelet network for breast cancer diagnosis, based on statistical features extracted from thermographic images of women's breasts.. Journal of Modeling in Engineering, 2023; 21(74): 51-64. doi: 10.22075/jme.2023.29162.2368
)