An improved breast cancer classification with hybrid chaotic sand cat and Remora Optimization feature selection algorithm.

ABSTRACT: Breast cancer is one of the most often diagnosed cancers in women, and identifying breast cancer histological images is an essential challenge in automated pathology analysis. According to research, the global BrC is around 12% of all cancer cases. Furthermore, around 25% of women suffer from BrC. Consequently, the prediction of BrC depends critically on the quick and precise processing of imaging data. The primary reason deep learning models are used in breast cancer detection is that they can produce findings more quickly and accurately than current machine learning-based techniques. Using a BreakHis dataset, we demonstrated in this work the viability of automatically identifying and classifying BrC. The first stage is pre-processing, which employs an Adaptive Switching Modified Decision

SUBMITTER: Alhassan AM 

PROVIDER: S-EPMC11008855 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature