Analysis of diverse optimisation algorithms in breast cancer detection

Breast cancer is a widespread problem faced by the women in recent years. It is highly essential to detect the breast cancer at an early stage to save lives. Image segmentation technique is used to segment the mistrustful masses from an ultrasound image of the breast. This work focuses on implementation and analysis of various optimisation algorithms in detecting mistrustful masses in the given ultrasound image of the breast. In preprocessing the speckle noise is reduced by using the median filter and contrast is improved by using adaptive histogram equalisation. Particle swarm optimisation, chaotic particle swarm optimisation (CPSO), k-medoids clustering, fuzzy c-means and k-means clustering are used in our work. A comparative analysis has been done using MATLAB and, it is proved that the CPSO has the best result among the others. The accuracy and dice similarity coefficient of the CPSO based method is 93.5793 and 0.8735 respectively.