An automatic feature selection and classification framework for analyzing ultrasound kidney images using dragonfly algorithm and random forest classifier

Abstract

In medical imaging, the automatic diagnosis of kidney carcinoma has become more difficult because it is not easy to detect by physicians. Pre-processing is the first identification method to enhance image quality, remove noise and unwanted components from the backdrop of the kidneys image. The pre-processing method is essential and significant for the proposed algorithm. The objective of this analysis is to recognize and classify kidney disturbances with an ultrasound scan by providing a number of substantial content description parameters. The ultrasound pictures are prepared to protect the interest pixels before extracting the feature. A series of quantitative features were synthesized of each images, the principal component analysis was conducted for minimizing the number of features to produce set of wavelet-based multi-scale features. Dragonfly algorithm (DFA) was executed in this method. In the proposed work, the design and training of a random decision forest classifier and selected features are implemented. The classification of e-health information using ideal characteristics is used by the RF classifier. The proposed technique is activated in MATLAB/simulink work site and the experimental results show that the peak accuracy of the proposed technique is 95.6% using GWO-FFBN techniques compared to other existing techniques.