Breast Cancer Prediction and Control Using BiLSTM and Two-Dimensional Convolutional Neural Network

Breast Cancer Prediction and Control Using BiLSTM and Two-Dimensional Convolutional Neural Network

Moses A. Agana, Chukwuemeka Odi Agwu, Nsinem A. Ukpoho
Copyright: © 2023 |Pages: 19
DOI: 10.4018/IJSI.316169
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Breast cancer has a devastating effect on women. Different strategies of breast cancer classification exist with minimal work done on the prediction of the occurrence of the disease in potential carriers. In this study, a breast cancer predictive system has been developed using bidirectional long short-term memory (BiLSTM) for feature extraction and learning while the two-dimensional convolutional neural network (CNN) was used for breast cancer classification. Histopathological images were used for cancer prediction. Python was used as the programming language for implementing the system. The model was tested using datasets from The Cancer Imaging Archive (TCIA) repository. An accuracy level of 98.8% (higher than the most recent existing model) was achieved for the prediction of the future occurrence of breast cancer based on the tests on the dataset. The application of the model using live data from women can help in the prediction and control of the occurrence of breast cancer amongst women.
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Breast cancer is a form of invasive cancer and one of the most common health problems for women that is globally responsible for a large number of deaths (Ali, 2019). According to the World Health Organisation’s projection, every year, the estimated number of breast cancer diagnosis amongst women is up to 1.5 million (WHO, 2014). Factors contributing to breast cancer mortality have been a topic of intense research and discussion in the scientific world. There is, however, a dearth of information on the incidence of breast cancer mortality in most resource-poor countries. Available data from most African workers on breast cancer focused on incidence, risk factors, and complications rather than mortality (Mohammed et. al., 2017). Breast cancer is believed to affect one in eight women during their lives. It is the commonest site specific malignancy affecting women and the most common cause of cancer mortality in women worldwide. Cancer is a disease in which abnormal cells grow in an uncontrolled way (Siegel et. al., 2016). Breast cancer is a malignant (cancerous) growth that begins in the tissues of the breast. It is the most common cancer in women, but it can also appear in men. Breast cancer is now an epidemic, posing a serious threat to the health of women of all races globally. In Nigeria, for instance, breast cancer is the leading cause of cancer related deaths among women. This is not due to a reduction in cervical cancer but an increase in the incidence of breast cancer. Breast cancer is commonly seen in four stages that represents its progression (Lee et al., 2010).

In stage-1, the disease is confined entirely to the breast. The cancer usually starts as a very tiny growth that cannot yet be felt but can be detected with imaging tests such as mammography and ultrasound. At this first stage, treatment is usually curative and more than 95% of those so detected will survive the disease beyond 5 years (Egenti, 2016). Stage II is a cancer that has involved lymph nodes in the armpit of the same side of the breast, while stage III disease is one that involves the muscles under the breast. Stages II and III therefore require very aggressive treatment using different modalities to contain the spread of the disease. It is however difficult to cure a patient in stage IV because the disease has spread and may have involved other organs in the body such as the lungs, liver, bones, the brain or the spine (Lorena et al., 2011: Egenti, 2016). The five-year survival rate for breast cancer patients in the United States exceeds 85%, in Nigeria it is a dismal 10% and is responsible for about 16% of all cancer related deaths (Mohammed et al, 2017).

To reduce death rate resulting from the disease, early detection and diagnosis are critical. Better diagnostic tools and method can minimize the fatality rate. Breast cancer diagnosis allows identifying the cancer cells; if the diagnosis tools become more efficient, then the detection and prediction can be more effective (Giu & Jyh-Cheng 2015: Karabatak, 2015).

Machine Learning (ML) is a subfield of Artificial Intelligence (AI) which allows machines to learn with or without the intervention of a human. Machine Learning has multiple potential applications in medicine and has been applied to a wide variety of oncology tasks, such as predicting disease susceptibility, survival rates and treatments. In the field of AI, ML is one of the most popular models which has been implemented rapidly to train machines and develop predictive models for effective decision making. In classification and prediction problems, Machine Learning methods are the leading methods for obtaining a better outcome. In cancer research, the ML methods could be used for identification and prediction of cancer. These ML methods could predict whether cancer is malignant or benign (Konanenko, 2001).

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