On July 26 – 2021, Eng. Omar Farag; Eng. Ahmed Kotb; and Eng. Mohamed Adel , former ONE Lab Summer 2018 Internship students, Eng. Mostafa Adel, and Eng. Ahmed Anwar, former ONE Lab Research Assistants, have an accepted paper at Taylor & Francis Cogent Engineering Journal titled “Early Breast Cancer Diagnostics Based on Hierarchical Machine Learning Classification for Mammography Images“.
This paper is a collaboration among Zewail City of Science and Technology, Cairo University, Nile University, German University in Cairo, and Ajman University.
Abstract: Breast cancer constitutes a significant threat to women’s health and is considered the second leading cause of their death. Breast cancer is a result of abnormal behavior in the functionality of the normal breast cells. Therefore, breast cells tend to grow uncontrollably, forming a tumor that can be felt like a breast lump. Early diagnosis of breast cancer is proved to reduce the risks of death by providing a better chance of identifying a suitable treatment. Machine learning and artificial intelligence play a key role in healthcare systems by assisting physicians in diagnosing early, better, and treating various diseases. For achieving the early detection of
breast cancer, this paper proposes a Machine Learning-based two-level top-down hierarchical approach for breast cancer detection and classification into three classes: normal, benign, and malignant, using the Mammographic Image Analysis Society (MIAS) mammography dataset. Different data preprocessing techniques are applied before using feature extraction techniques and machine learning algorithms for classification.
