A NEW APPROACH FOR BREAST CANCER DETECTION- BASED MACHINE LEARNING TECHNIQUE

Malek M. AL-NAWASHI

Nawashi@bau.edu.jo
Al-Balqa Applied University (Jordan)
https://orcid.org/0000-0001-5641-4892

Obaida M. AL-HAZAIMEH


a:1:{s:5:"en_US";s:27:"Al-Balqa Applied University";} (Jordan)
https://orcid.org/0000-0002-5231-8155

Mutaz Kh. KHAZAALEH


Al-Balqa Applied University (Jordan)
https://orcid.org/0000-0002-2071-7020

Abstract

The leading cause of cancer-related mortality is breast cancer. Breast cancer detection at an early stage is crucial.  Data on breast cancer can be diagnosed using a number of different Machine learning approaches. Automated breast cancer diagnosis using a Machine Learning model is introduced in this research.  Features were selected using Convolutional Neural Networks (CNNs) as a classifier model, and noise was removed using Contrast Limited Adaptive Histogram Equalization (CLAHE).  On top of that, the research compares five algorithms: Random Forest, SVM, KNN, Naïve Bayes classifier, and Logistic Regression. An extensive dataset of 3002 combined images was used to test the system. The dataset included information from 1400 individuals who underwent digital mammography between 2007 and 2015. Accuracy and precision are the metrics by which the system's performance is evaluated.   Due to its low computing power requirements and excellent accuracy, our suggested model is shown to be quite efficient in the simulation results.


Keywords:

machine learning, breast cancer, CNN, image processing, healthcare

Al-hazaimeh, O., Alomari, S. A., Alsakran, J., & Alhindawi, N. (2014). Cross correlation–new based technique for speaker recognition. Int J Acad Res, 6, 232-239.
  Google Scholar

Al-hazaimeh, O. M., Abu-Ein, A. A., Tahat, N. M., Al-Smadi, M. m. A., & Al-Nawashi, M. M. (2022). Combining Artificial Intelligence and Image Processing for Diagnosing Diabetic Retinopathy in Retinal Fundus Images. International Journal of Online & Biomedical Engineering, 18(13).
  Google Scholar

Al-Hazaimeh, O. M., Al-Nawashi, M., & Saraee, M. (2019). Geometrical-based approach for robust human image detection. Multimedia Tools and Applications, 78, 7029-7053.
  Google Scholar

Al-Hazaimeh, O. M., & Al-Smadi, M. (2019). Automated pedestrian recognition based on deep convolutional neural networks. International Journal of Machine Learning and Computing, 9(5), 662-667.
  Google Scholar

Al-Nawashi, M., Al-Hazaimeh, O. M., & Saraee, M. (2017). A novel framework for intelligent surveillance system based on abnormal human activity detection in academic environments. Neural Computing and Applications, 28, 565-572.
  Google Scholar

Alanazi, S. A., Kamruzzaman, M., Islam Sarker, M. N., Alruwaili, M., Alhwaiti, Y., Alshammari, N., & Siddiqi, M. H. (2021). Boosting breast cancer detection using convolutional neural network. Journal of Healthcare Engineering, 2021.
  Google Scholar

Alhindawi, N., Al-Hazaimeh, O. M., Malkawi, R., & Alsakran, J. (2016). A Topic Modeling Based Solution for Confirming Software Documentation Quality. International Journal of Advanced Computer Science and Applications, 7(2).
  Google Scholar

Barrios, C. H. (2022). Global challenges in breast cancer detection and treatment. The Breast, 62, S3-S6.
  Google Scholar

Carlson, R. W., Allred, D. C., Anderson, B. O., Burstein, H. J., Carter, W. B., Edge, S. B., . . . Giordano, S. H. (2011). Invasive breast cancer. Journal of the National Comprehensive Cancer Network, 9(2), 136-222.
  Google Scholar

Chang, P. J., Asher, A., & Smith, S. R. (2021). A targeted approach to post-mastectomy pain and persistent pain following breast cancer treatment. Cancers, 13(20), 5191.
  Google Scholar

Desai, M., & Shah, M. (2021). An anatomization on breast cancer detection and diagnosis employing multi-layer perceptron neural network (MLP) and Convolutional neural network (CNN). Clinical eHealth, 4, 1-11.
  Google Scholar

DeSantis, C. E., Ma, J., Gaudet, M. M., Newman, L. A., Miller, K. D., Goding Sauer, A., . . . Siegel, R. L. (2019). Breast cancer statistics, 2019. CA: a cancer journal for clinicians, 69(6), 438-451.
  Google Scholar

Fatima, N., Liu, L., Hong, S., & Ahmed, H. (2020). Prediction of breast cancer, comparative review of machine learning techniques, and their analysis. IEEE Access, 8, 150360-150376.
  Google Scholar

Gharaibeh, N., Abu-Ein, A. A., Al-hazaimeh, O. M., Nahar, K. M., Abu-Ain, W. A., & Al-Nawashi, M. M. (2023). Swin Transformer-Based Segmentation and Multi-Scale Feature Pyramid Fusion Module for Alzheimer's Disease with Machine Learning. International Journal of Online & Biomedical Engineering, 19(4).
  Google Scholar

Gharaibeh, N., Al-hazaimeh, O. M., Abu-Ein, A., & Nahar, K. (2021). A hybrid svm naïve-bayes classifier for bright lesions recognition in eye fundus images. International Journal on Electrical Engineering and Informatics, 13(3), 530-545.
  Google Scholar

Gharaibeh, N., Al-Hazaimeh, O. M., Al-Naami, B., & Nahar, K. M. (2018). An effective image processing method for detection of diabetic retinopathy diseases from retinal fundus images. International Journal of Signal and Imaging Systems Engineering, 11(4), 206-216.
  Google Scholar

Hall, K., Chang, V., & Mitchell, P. (2022). Machine Learning Techniques for Breast Cancer Detection. Paper presented at the COMPLEXIS.
  Google Scholar

Houssein, E. H., Emam, M. M., Ali, A. A., & Suganthan, P. N. (2021). Deep and machine learning techniques for medical imaging-based breast cancer: A comprehensive review. Expert Systems with Applications, 167, 114161.
  Google Scholar

Kharya, S., Dubey, D., & Soni, S. (2013). Predictive machine learning techniques for breast cancer detection. International journal of computer science and information Technologies, 4(6), 1023-1028.
  Google Scholar

Loibl, S., & Gianni, L. (2017). HER2-positive breast cancer. The Lancet, 389(10087), 2415-2429.
  Google Scholar

Lu, W., Jansen, L., Post, W., Bonnema, J., Van de Velde, J., & De Bock, G. (2009). Impact on survival of early detection of isolated breast recurrences after the primary treatment for breast cancer: a meta-analysis. Breast cancer research and treatment, 114, 403-412.
  Google Scholar

Ma'moun, A., Al-hazaimeh, O. M., Alhindawi, N., & Hayajneh, S. M. (2014). A dual curvature shell phased array simulation for delivery of high intensity focused ultrasound. Computer and Information Science, 7(3), 49.
  Google Scholar

Mahmood, T., Arsalan, M., Owais, M., Lee, M. B., & Park, K. R. (2020). Artificial intelligence-based mitosis detection in breast cancer histopathology images using faster R-CNN and deep CNNs. Journal of clinical medicine, 9(3), 749.
  Google Scholar

Melekoodappattu, J. G., Dhas, A. S., Kandathil, B. K., & Adarsh, K. (2023). Breast cancer detection in mammogram: Combining modified CNN and texture feature based approach. Journal of Ambient Intelligence and Humanized Computing, 14(9), 11397-11406.
  Google Scholar

Nahar, K., Al-Hazaimeh, O., Abu-Ein, A., & Gharaibeh, N. (2020). Phonocardiogram classification based on machine learning with multiple sound features. Journal of Computer Science, 16(11), 1648-1656.
  Google Scholar

Nahar, K., Alhindawi, N., Al-Hazaimeh, O., Alkhatib, R., & Al-Akhras, A. (2018). NLP and IR based solution for confirming classification of research papers. Journal of Theoretical and Applied Information Technology, 96(16), 5269-5279.
  Google Scholar

Nallamala, S. H., Mishra, P., & Koneru, S. V. (2019). Breast cancer detection using machine learning approaches. International Journal of Recent Technology and Engineering, 7(5), 478-481.
  Google Scholar

Nanda, K., Bastian, L. A., & Schulz, K. (2002). Hormone replacement therapy and the risk of death from breast cancer: a systematic review. American journal of obstetrics and gynecology, 186(2), 325-334.
  Google Scholar

Narod, S. A., Iqbal, J., Giannakeas, V., Sopik, V., & Sun, P. (2015). Breast cancer mortality after a diagnosis of ductal carcinoma in situ. JAMA oncology, 1(7), 888-896.
  Google Scholar

Nguyen, C., Wang, Y., & Nguyen, H. N. (2013). Random forest classifier combined with feature selection for breast cancer diagnosis and prognostic.
  Google Scholar

Rajakumari, R., & Kalaivani, L. (2022). Breast Cancer Detection and Classification Using Deep CNN Techniques. Intelligent Automation & Soft Computing, 32(2).
  Google Scholar

Reza, A. M. (2004). Realization of the contrast limited adaptive histogram equalization (CLAHE) for real-time image enhancement. Journal of VLSI signal processing systems for signal, image and video technology, 38, 35-44.
  Google Scholar

Rivera-Franco, M. M., & Leon-Rodriguez, E. (2018). Delays in breast cancer detection and treatment in developing countries. Breast cancer: basic and clinical research, 12, 1178223417752677.
  Google Scholar

Sivapriya, J., Kumar, A., Sai, S. S., & Sriram, S. (2019). Breast cancer prediction using machine learning. International Journal of Recent Technology and Engineering (IJRTE), 8(4), 4879-4881.
  Google Scholar

Svensson, B., Dylke, E., Ward, L., Black, D., & Kilbreath, S. L. (2020). Screening for breast cancer–related lymphoedema: self-assessment of symptoms and signs. Supportive Care in Cancer, 28, 3073-3080.
  Google Scholar

Tagliafico, A. S., Piana, M., Schenone, D., Lai, R., Massone, A. M., & Houssami, N. (2020). Overview of radiomics in breast cancer diagnosis and prognostication. The Breast, 49, 74-80.
  Google Scholar

Tanabe, K., Ikeda, M., Hayashi, M., Matsuo, K., Yasaka, M., Machida, H., . . . Hirasawa, T. (2020). Comprehensive serum glycopeptide spectra analysis combined with artificial intelligence (CSGSA-AI) to diagnose early-stage ovarian cancer. Cancers, 12(9), 2373.
  Google Scholar

Tiwari, M., Bharuka, R., Shah, P., & Lokare, R. (2020). Breast cancer prediction using deep learning and machine learning techniques. Available at SSRN 3558786.
  Google Scholar

Vaka, A. R., Soni, B., & Reddy, S. (2020). Breast cancer detection by leveraging Machine Learning. Ict Express, 6(4), 320-324.
  Google Scholar

Vasundhara, S., Kiranmayee, B., & Suresh, C. (2019). Machine learning approach for breast cancer prediction. International Journal of Recent Technology and Engineering (IJRTE), 8(1).
  Google Scholar

Wang, Z., Li, M., Wang, H., Jiang, H., Yao, Y., Zhang, H., & Xin, J. (2019). Breast cancer detection using extreme learning machine based on feature fusion with CNN deep features. IEEE Access, 7, 105146-105158.
  Google Scholar

Wilkinson, L., & Gathani, T. (2022). Understanding breast cancer as a global health concern. The British Journal of Radiology, 95(1130), 20211033.
  Google Scholar

Yassin, N. I., Omran, S., El Houby, E. M., & Allam, H. (2018). Machine learning techniques for breast cancer computer aided diagnosis using different image modalities: A systematic review. Computer methods and programs in biomedicine, 156, 25-45.
  Google Scholar

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Published
2024-03-30

Cited by

AL-NAWASHI , M. M., AL-HAZAIMEH, O. M., & KHAZAALEH , M. K. (2024). A NEW APPROACH FOR BREAST CANCER DETECTION- BASED MACHINE LEARNING TECHNIQUE. Applied Computer Science, 20(1), 1–16. https://doi.org/10.35784/acs-2024-01

Authors

Malek M. AL-NAWASHI  
Nawashi@bau.edu.jo
Al-Balqa Applied University Jordan
https://orcid.org/0000-0001-5641-4892

Authors

Obaida M. AL-HAZAIMEH 

a:1:{s:5:"en_US";s:27:"Al-Balqa Applied University";} Jordan
https://orcid.org/0000-0002-5231-8155

Authors

Mutaz Kh. KHAZAALEH  

Al-Balqa Applied University Jordan
https://orcid.org/0000-0002-2071-7020

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