Classification of Malignant or Benign Cancer using Neural Networks
Gaby Abou Haidar, Roger Achkar, David Semaan, Michel J. Owayjan · 2023
Cancer detection and predetection are being highly researched and studied recently due to the high density spread of this disease. It occurs when abnormal cells divide continuously without limitation where it can spread randomly to different parts of the body. Different types of cancer are recorded targeting the blood, lungs, bones, liver, and many other organs. People with cancer have similar symptoms such as lumps, abnormal bleeding, eating problems, fatigue, weight loss, and some more severe issues. Breast Cancer, which will be addressed in this work, is a type of cancer that hits women in general. It is dangerous especially when cells around the breast area overgrow and cause lumps on the breasts. In order to pre-detect and try to avoid breast cancer., it's important to perform annual mammography check. This work is aimed to create a classification system that uses neural network algorithms to conclude whether a certain tumor is a malignant or a benign tumor. Back Propagation algorithm as well as supervised learning were used and tested with highly satisfying successful classification.