Development of Breast Cancer Diagnosis System

Keywords: Breast cancer, Breast Cancer Diagnosis System (BCDS), Early detection, and Healthcare professionals.


Breast cancer poses a global health challenge, necessitating advanced diagnostic system for improved patient outcomes. This study introduces the Breast Cancer Diagnosis System (BCDS), employing sophisticated programming languages such as JavaScript, React and Python to develop an advanced expert system for a swift and precise breast cancer diagnosis. Emphasizing accuracy, early detection and informed decision-making. BCDS addresses the intricate nature of breast cancer diagnosis. Its comprehensive solution harnesses the capabilities of powerful programming languages to prioritize efficiency and precision, aiming to enhance the diagnostic process for healthcare professionals. Rigorous testing ensures the reliability of independent modules, logical decisions, and data validation. BCDS demonstrates promising outcomes with a user friendly landing page and clear operational guide. This system emerges as a valuable contribution to healthcare technology, addressing the complexities of breast cancer diagnosis and care, thereby signifying a significant stride in breast cancer diagnostic systems and underscoring the ongoing need for advancements in healthcare technology. In furthering this study, expanding validation studies by incorporating larger and more diverse dataset in tackling complex challenges on breast cancer would help in creating more awareness for early detection.  

Author Biography

Kehinde A. Sotonwa, Lagos State University
Computer Science and Information Technology


Acs, B., Hartman, J., Sönmez, D., Lindman, H., Johansson, A. L., & Fredriksson, I. (2024). Real-world overall survival and characteristics of patients with ER-zero and ER-low HER2-negative breast cancer treated as triple-negative breast cancer: a Swedish population-based cohort study. The Lancet Regional Health–Europe, 40.

Adetifa, F., & Ojikutu, R. K. (2009). Prevalence and trends in breast cancer in Lagos State, Nigeria. African Research Review, 3(5).

Arnold, M., Morgan, E., Rumgay, H., Mafra, A., Singh, D., Laversanne, M., . . . Siesling, S. (2022). Current and future burden of breast cancer: Global statistics for 2020 and 2040. The Breast, 66, 15-23.

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

Bartsch, R., Rinnerthaler, G., Petru, E., Egle, D., Gnant, M., Balic, M., . . . Singer, C. (2023). Updated Austrian treatment algorithm for metastatic triple-negative breast cancer. Wiener klinische Wochenschrift, 1-15.

Bray, F., Ferlay, J., Soerjomataram, I., Siegel, R. L., Torre, L. A., & Jemal, A. (2018). Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA: a cancer journal for clinicians, 68(6), 394-424.

Casal-Guisande, M., Álvarez-Pazó, A., Cerqueiro-Pequeño, J., Bouza-Rodríguez, J.-B., Peláez-Lourido, G., & Comesaña-Campos, A. (2023). Proposal and definition of an intelligent clinical decision support system applied to the screening and early diagnosis of breast cancer. Cancers, 15(6), 1711.

Casal-Guisande, M., Comesaña-Campos, A., Dutra, I., Cerqueiro-Pequeño, J., & Bouza-Rodríguez, J.-B. (2022). Design and development of an intelligent clinical decision support system applied to the evaluation of breast cancer risk. Journal of personalized medicine, 12(2), 169.

Ferlay, J., Colombet, M., Soerjomataram, I., Mathers, C., Parkin, D. M., Piñeros, M., . . . Bray, F. (2019). Estimating the global cancer incidence and mortality in 2018: GLOBOCAN sources and methods. International journal of cancer, 144(8), 1941-1953.

Liew, X. Y., Hameed, N., & Clos, J. (2021). A review of computer-aided expert systems for breast cancer diagnosis. Cancers, 13(11), 2764.

Mohamed, S., Wahbi, T., & Sayed, M. (2018). Automated detection and classification of breast cancer using mammography images. International Journal of Science, Engineering and Technology Research (IJSETR), 7(4).

Nair, N., Shet, T., Parmar, V., Havaldar, R., Gupta, S., Budrukkar, A.,. . . Yadav, P. (2018). Breast cancer in a tertiary cancer center in India-An audit, with outcome analysis. Indian journal of cancer, 55(1), 16-22.

Okobia, M. N., Bunker, C. H., Okonofua, F. E., & Osime, U. (2006). Knowledge, attitude and practice of Nigerian women towards breast cancer: a cross-sectional study. World journal of surgical oncology, 4, 1-9.

Singh, E., Joffe, M., Cubasch, H., Ruff, P., Norris, S., & Pisa, P. (2017). Breast cancer trends differ by ethnicity: a report from the South African National Cancer Registry (1994–2009). The European Journal of Public Health, 27(1), 173-178.

Singh, T., Bhadauria, S., Wadhwani, S., & Wadhwani, A. (2010). Design issues of expert system for breast cancer detection. International Journal of Information Studies, 3(1), 198-204.

Suchy, S. L., Landreneau, R. J., Schuchert, M. J., Wang, D., Ervin Jr, P. R., & Brower, S. L. (2013). Adaptation of a chemosensitivity assay to accurately assess pemetrexed in ex vivo cultures of lung cancer. Cancer biology & therapy, 14(1), 39-44.

Sung, H., Ferlay, J., Siegel, R. L., Laversanne, M., Soerjomataram, I., Jemal, A., & Bray, F. (2021). Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA: a cancer journal for clinicians, 71(3), 209-249.

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

Yang, C., Lei, C., Zhang, Y., Zhang, J., Ji, F., Pan, W., . . . Li, J. (2020). Comparison of overall survival between invasive lobular breast carcinoma and invasive ductal breast carcinoma: a propensity score matching study based on SEER database. Frontiers in Oncology, 10, 590643