Detection of Breast Cancer using Curvelet Transform and Adaptive Particle Swarm Optimization Technique

The breast cancer is the most prevalent malignancy. Better chance of curing breast cancer is early detection, which can also lower mortality rates. The best technique for early breast disease detection is the mammography. In the suggested approach, curvelet transform is utilized to extract features, and adaptive particle swarm optimization helps to choose the eminent features. Adaptive Particle Swarm optimization has been devised to speed up and simplify the process of feature selection and Support Vector Machine (SVM) aids in breast cancer classification. We present an Adaptive Particle Swarm optimization (APSO) that outperforms Particle Swarm optimization (PSO) regarding search efficiency. The suggested model is examined using a collection of 332 images from the Mammographic Image Analysis Society (MIAS) database. The executed findings are compared with the old transforms, and the results demonstrate that the suggested model has higher detection accuracy rates than the earlier approaches.