Application of a New Hybrid Machine Learning (Fuzzy-PSO) for Detection of Breast’s Tumor
Hamzeh Ghorbani, Sahar Lajmorak, Simin Ghorbani, Parvin Ghorbani, Nina Khlghatyan, Harutyun Stepanyan, Samaneh Bahrami, Seyed Mohammad Rasaei, Mehdi Ahmadi Alvar, Rituraj Rituraj
Abstract
Breast cancer is the second leading cause of death after lung cancer. The only possible way to save patients' lives is early diagnosis of the disease; Because if this disease is diagnosed in the early stages and with a high level of accuracy, the chance of survival increases. Different fuzzy-based soft computing techniques have been proposed. In this research, the proposed fuzzy hybrid algorithm - particle swarm has been used to detect the type of breast tumors based on the analysis of features in mammography images. The proposed method in this study, the fuzzy hybrid algorithm - the proposed particle swarm algorithm, has a remarkable performance of 94.58% in breast cancer diagnosis. The results obtained from this study can be used for timely diagnosis and providing effective treatments for breast cancer.