Mitosis Detection from Breast Cancer Histology Slide Images using Particle Swarm Optimization and Support Vector Machine
Keywords:
breast cancer, classification, feature extraction, mitosis detection, Particle Swarm Optimization (PSO), Support vector classification (SVM), Complete Local Binary Pattern (CLBP)Abstract
This paper introduces a new strategy for the purpose of automatic mitosis detection from breast cancer histopathology slide images. In this method, a new approach for reducing the number of false positive using Particle Swarm Optimization (PSO) is proposed. The proposed system is implemented on the histopathology slide images acquired by Aperio XT scanner (scanner A). In PSO algorithm the number of false positive objects or non-mitosis are defined as a cast function and by the minimization it the most of the non-mitosis candidates will be removed. Then some color, texture features such as co-occurrence and run
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