Computer-aided diagnosis for early cancer detection using Adaptive Kernel Based Fuzzy Cuckoo Search Optimization Clustering from mammogram images

•Early detection of breast cancer is major issue that must be addressed globally.•Breast cancer detection is reliant on segmentation techniques.•Segmentation process play key role in detection.•It is the process by which a digital image is divided into various meaningful sections. Early detection of breast cancer is major issue that must be addressed globally. Segmentation process play key role in detection. We propose a new hybrid approach called Adaptive Kernel Based Fuzzy Cuckoo Search Optimization Clustering to segment mammogram images. It is diversified into two stages. A kernel-based fuzzy c-means algorithm with cuckoo search optimization is used to obtain optimal cluster centers and obtained optimum centers act as centers to the KFCM clustering process to get different segmented regions in pre processing stage. In post processing stage, a level set method is employed to minimize boundary leakages. The simulation was performed on RIDER data set. The proposed approach is validated by calculating similarity indexing parameters. The simulation was performed on 12 distinct images from the RIDER mammography database. The suggested method's experimental results were compared to existing level set approaches such as IVC2010, IVC2013, and ESA2021.