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Predicting breast cancer in fine needle aspiration images using machine learning
(Institute of Electrical and Electronics Engineers Inc., 2020) Al-Sammarraie, Luay Hani Abbas; Ibrahim, Abdullahi Abdu
Breast cancer is the second leading cause of cancer death in women in the world. Statistics show that 1, 152, 161 new cases of breast cancer are found worldwide; and with 411, 093 deaths It has been shown that early diagnosis of breast cancer increases the probability of a complete recovery and reduces the mortality of patients suffering from this cancer [1] Cancer is the mutation of genes responsible for cell replication and the regulation of cell growth. These genes are found in the nucleus of cells and act as a control to turn different cells on or off so that old cells die while new ones take over. When a mutation occurs, these cells do not die and begin to divide uncontrollably, creating tumors in this work we have designed and implemented a system whose main purpose is to detect the existence of breast cancer lumps in fine needle aspiration images. We use a clustering and a feature extraction method such as CNN and then we use a classification method to detect the cancer. Early detection of breast cancer is essential to increase patient survival © 2020 IEEE.