Authors : V. E. Ejiofor; Chukwuma Felicia Ngozi; Ugoh Daniel; Hillary Uchenna Amulu

Volume/Issue : Volume 9 - 2024, Issue 6 - June

Google Scholar : https://tinyurl.com/584tc858

Scribd : https://tinyurl.com/bdzfyfyh

DOI : https://doi.org/10.38124/ijisrt/IJISRT24JUN1039

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : Breast cancer is becoming one of the most common diseases among women and is a growing global concern. A significant number of women have died worldwide from breast cancer. Studies suggest that early detection gives one better chance at treatment and management. However, the major challenges in early detection of breast cancer are awareness issues and patients’ insensitivity about the disease. This implies that regular breast examination leads to early detection of signs and symptoms of breast cancer. This exercise has been challenged with awareness, improper ways of conducting it and reporting of signs and symptom to appropriate quarters. This work is aimed at designing and development of a computer assisted system that runs on both desktop and mobile device(s) to assist women in conducting self- breast examination. To achieve this, object oriented analysis and design methodology (OOADM) was adopted for investigation and implementation. This work was designed and implemented in Microsoft Visual Studio while MySQL was used as the database management (DBMS). The rule- based approach was used for classification of breast abnormality. The result is an eHealth information system for early detection of breast cancer and patient monitoring. This will assist women to properly conduct self-breast examination, upload signs and symptoms discovered and enable medical professionals monitor patients.

Keywords : Cancer, Fibrosis, Mastectomy, Lymph Nodes, Oncology, Mammography, Telemedicine, Receptor.

References :

1. Albarqouni, S., Baur, C., Achilles, F., Belagiannis, V., Demirci, S., & Navab, N. (2016). Deep learning from crowds for mitosis detection in breast cancer histology images. IEEE Transactions on Medical Imaging, 35(5), 1313–1321.
2. Anooj, P.K. (2012). Clinical support system: Risk level prediction of heart disease using weighted fuzzy rules. Journal of King Saud University. 24
3. Baumgartner, C. F., Kamnitsas, K., Matthew, J., Smith, S., Kainz, B., & Rueckert, D. (2016). .Real-time standard scan plane detection and localization in fetal ultrasound using fully convolutional neural networks. International conference on medical image computing and computer-assisted intervention, 203–211.
4. De Vos, B. D., Berendsen, F. F., Viergever, M. A., Staring, M., & Iˇsgum, I. (2017). End-to-end unsupervised deformable image registration with a convolutional neural network. Deep learning in medical image analysis and multimodal learning for clinical decision support, 204–212.
5. Duan, J., Bello, G., Schlemper, J., Bai, W., Dawes, T. J., Biffi, C., et al. (2019). Automatic 3D bi-ventricular segmentation of cardiac images by a shape-refined multi-task deep learning approach. IEEE Transactions on Medical Imaging, 38(9), 2151–2164.
6. Faust, Oliver, Hagiwara, Yuki, Hong, Tan Jen, Lih, Shu Oh, and Acharya, Rajendra U. Deep learning for healthcare applications based on physiological signals: A review. .            www.elsevier.com/locate/cmpb
7. Gao, X., Li, W., Loomes, M., & Wang, L. (2017). A fused deep learning architecture for viewpoint classification of echocardiography.  Information Fusion, 36, 103–113.
8. Ghesu, F. C., Krubasik, E., Georgescu, B., Singh, V., Zheng, Y., Hornegger, J., et al.
9. (2016a). Marginal space deep learning: Efficient architecture for volumetric image parsing. IEEE Transactions on Medical Imaging, 35(5), 1217–1228.
10. Ghesu, F. C., Krubasik, E., Georgescu, B., Singh, V., Zheng, Y., Hornegger, J.,et  al.(2016b). Marginal space deep learning: Efficient architecture for volumetric image parsing. IEEE Transactions on Medical Imaging, 35(5), 1217–1228.
11. Guo, Y., Gao, Y., & Shen, D. (2015). Deformable MR prostate segmentation via deep feature learning and sparse patch matching. IEEE Transactions on Medical Imaging, 35 (4), 1077–1089.
12. Guo, Z., Li, X., Huang, H., Guo, N., & Li, Q. (2019). Deep learning-based image segmentation on multimodal medical imaging. IEEE Transactions on Radiation and Plasma Medical Sciences, 3(2), 162–169.
13. Haskins, G., Kruger, U., & Yan, P. (2020). Deep learning in medical image registration: A survey. Machine Vision and Applications, 31(1), 8.
14. Huang, W., Luo, M., Liu, X., Zhang, P., Ding, H., Xue, W., et al.  (2019). Arterial spin labeling images synthesis from  structural magnetic resonance imaging using unbalanced deep discriminant learning. IEEE Transactions on Medical Imaging, 38 (10), 2338–2351.
15. Lundervold, A. S., & Lundervold, A. (2019). An overview of deep learning in medical imaging focusing on MRI. Zeitschrift für Medizinische Physik, 29(2), 102–127.
16. Oktay, O., Ferrante, E., Kamnitsas, K., Heinrich, M., Bai, W., Caballero, J., et al. (2017).
17. Anatomically constrained neural networks (ACNNs):  Application to cardiac image enhancement and segmentation. IEEE Transactions on Medical Imaging, 37(2), 384–395.
18. Shen, W., Zhou, M., Yang, F., Yang, C., & Tian, J. (2015). Multi-scale convolutional neural networks for lung nodule classification. International conference on information processing in medical imaging, 588–599.
19. Shen, D., Wu, G., & Suk, H.-I.  (2017). Deep learning in medical image analysis. Annual Review of Biomedical Engineering, 19, 221–248.
20. Zhu, Q., Du, B., & Yan, P. (2019). Boundary-weighted domain adaptive neural network for prostate MR image segmentation. IEEE Transactions on Medical Imaging, 39(3), 753–763.