Anas Bilal, Azhar Imran, Xiaowen Liu, Xiling Liu, Zohaib Ahmad, Muhammad Shafiq, Ahmed M El-Sherbeeny, Haixia Long
The timely and accurate diagnosis of breast cancer is pivotal for effective treatment, but current automated mammography classification methods have their constraints. In this study, we introduce an innovative hybrid model that marries the power of the Extreme Learning Machine (ELM) with FuNet transfer learning, harnessing the potential of the MIAS dataset. This novel approach leverages an Enhanced Quantum-Genetic Binary Grey Wolf Optimizer (Q-GBGWO) within the ELM framework, elevating its performance. Our contributions are twofold: firstly, we employ a feature fusion strategy to optimize feature extraction, significantly enhancing breast cancer classification accuracy...
April 24, 2024: Computers in Biology and Medicine