Breast cancer detection and classification using metaheuristic optimized ensemble extreme learning machine

Breast cancer deaths are increasing rapidly due to the abnormal growth of breast cells in the women's milk duct. Manual cancer diagnosis from mammogram images is also difficult for radiologists and medical practitioners. This paper proposes a novel metaheuristic algorithm-based machine learning model and Fuzzy C Means-based segmentation technique for the classification and detection of breast cancer from mammogram images. At first instance, the fuzzy factor improved fast and robust fuzzy c means (FFI-FRFCM) segmentation is proposed for the segmentation by modifying the member partition matrix of the FRFCM technique. Secondly, a hybrid improved water cycle algorithm-Accelerated particle swarm optimization (IWCA-APSO) optimization, is proposed for weight optimization of the ensemble extreme learning machine (EELM) model. Three benchmark functions are taken for optimization to demonstrate the proposed hybrid IWCA-APSO algorithm's uniqueness. With the INbreast dataset, the IWCA-APSO-based EELM classification shown the sensitivity, specificity, accuracy, and computational time as 99.67%, 99.71%, 99.36%, and 23.8751 s respectively. The proposed IWCA-APSO-based EELM model performs better than the traditional models at classifying breast cancer. © 2023, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of Computer Applications and Management.