Breast cancer detection model using fuzzy entropy segmentation and ensemble classification

•Exploits the advanced Self Improved Cat Swarm Optimization (SI-CSO) to propose a new Breast Cancer Detection Model.•Promotes SI-CSO based optimization, where includes “(i) Pre-processing (ii) Segmentation (iii) Feature extraction (iv) Optimal feature selection and (ii) Classification”.•The performance of SI-CSO based Detection model is better than standard ensemble + WOA, ensemble + MFO, ensemble + GA and ensemble + CSO models. According to studies, breast cancer is the deadliest disease and the main cause of the elevated mortality rates among women. The main method for detecting breast cancer is mammography. Even today, using a mammography image to make earlier detection of breast cancer remains a complex undertaking. In this work, the proposed system includes the following processes: “(i) pre-processing (ii) segmentation (iii) feature extraction (iv) optimal feature selection and (v) classification”. Initially, RGB-Grey scale conversion is done during pre-processing, and segmentation is carried out using the proposed fuzzy entropy segmentation model. Then the features including “fractal features like lacunarity, fractal dimension and texture features such as Grey Level Co-occurrence Matrix (GLCM) and Proposed Local Binary Patterns (LBP)” are determined. Then, in the prediction phase, an optimized ensemble classifier is introduced. The proposed ensemble classifier is constructed by amalgamating the “Random Forest (RF), Support Vector Machine (SVM) Neural Network (NN) and Optimized CNN” respectively. The extracted features are subjected to SVM, NN and RF, simultaneously. Here, optimized CNN determines the final results. In particular, Self Improved Cat Swarm Optimization (SI-CSO) is used to fine-tune CNN's weights. Finally, many metrics demonstrate the proposed model's efficiency.