Enhanced Grey Wolf Optimization based Hyper-parameter optimized Convolution Neural Network for Kidney Image Classification

Abstract

Over the last few years, Convolution Neural Networks (CNN) have shown dominant performance over real world applications due to their ability to find good solutions and deal with image data. However their performance is highly dependent on the network architecture and methods for optimizing their hyper parameters especially number and size of filters. Designing a good CNN architecture requires human expertise and domain knowledge. So, it is difficult in CNN to find sufficient number and size of filters for classification problems. The standard GWO algorithm used for any optimization purpose suffers from some issues such as slow convergence speed, trapping in local minima and unable to maintain balance between exploration and exploitation. In order to have proper balance between these phases, two modifications in GWO are introduced in this paper. A technique for finding optimum CNN architecture using methods based on Enhanced Grey Wolf Optimization (E-GWO) is proposed. The paper presents optimization of hyper parameters (numbers and size of filters in convolution layer) of CNN using E-GWO to improve the performance of the model. Kidney ultrasound images dataset collected from ultrasound centre is used to evaluate the performance of the proposed algorithm. Experimental results showed that optimization of CNN with E-GWO outperformed CNN optimized with traditional GA, PSO and GWO and conventional CNN yielding 97.01% accuracy. At last, the obtained results are statistically validated using t-test.