Hybrid Wolf Pack Algorithm and Particle Swarm Optimization Algorithm for Breast Cancer Diagnosis
Hybrid WPA and PSO Algorithm
Keywords:Breast Cancer, Feature Extraction, Mamaogram Image, Neural Network, Segmentation
In women, breast cancer is deadly disease which is increased the death rate of women. By exploiting the mammogram images, a precise and early recognition of breast cancer is a complex task. Therefore, a new breast cancer recognition technique was proposed that considered five important stages: segmentation, preprocessing, feature extraction, feature selection as well as classification. Initially, by exploiting the median filtering as well as Contrast Limited Adaptive Histogram Equalization (CLAHE), input mammogram images are preprocessed. Subsequently, through the region growing method, the preprocessed images are fed to segmentation. Then, from the segmented image, texture, geometric and gradient features are extracted. The feature vector length is higher, it is important to choose optimal features. Moreover, the optimal features chosen are performed using the proposed optimization method. After completing the selection of the optimal features, they are fed to the classification procedure including the Neural Network (NN) classifier. As an innovation, to improve the precision of diagnosis (benign as well as malignant), the NN weight is chosen optimally. The NN weight optimization and the optimal feature selection are attained using the Hybrid Wolf Pack Algorithm (WPA) and Particle Swarm Optimization (PSO) Algorithm called the Hybrid WPA-PSO algorithm. At last, the performance analysis is performed between the proposed and conventional techniques.
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