Chaotic Harris Hawks Optimization with Quasi-Reflection-Based Learning: An Application to Enhance CNN Design.

Jameer Basha,Pavel Trojovský,Nebojsa Bacanin,Miodrag Zivkovic,Nikola Vukobrat,Stepan Hubálovský,K Venkatachalam

doi:10.3390/s21196654

Abstract

The research presented in this manuscript proposes a novel Harris Hawks optimization algorithm with practical application for evolving convolutional neural network architecture to classify various grades of brain tumor using magnetic resonance imaging. The proposed improved Harris Hawks optimization method, which belongs to the group of swarm intelligence metaheuristics, further improves the exploration and exploitation abilities of the basic algorithm by incorporating a chaotic population initialization and local search, along with a replacement strategy based on the quasi-reflection-based learning procedure. The proposed method was first evaluated on 10 recent CEC2019 benchmarks and the achieved results are compared with the ones generated by the basic algorithm, as well as with results of other state-of-the-art approaches that were tested under the same experimental conditions. In subsequent empirical research, the proposed method was adapted and applied for a practical challenge of convolutional neural network design. The evolved network structures were validated against two datasets that contain images of a healthy brain and brain with tumors. The first dataset comprises well-known IXI and cancer imagining archive images, while the second dataset consists of axial T1-weighted brain tumor images, as proposed in one recently published study in the Q1 journal. After performing data augmentation, the first dataset encompasses 8.000 healthy and 8.000 brain tumor images with grades I, II, III, and IV and the second dataset includes 4.908 images with Glioma, Meningioma, and Pituitary, with 1.636 images belonging to each tumor class. The swarm intelligence-driven convolutional neural network approach was evaluated and compared to other, similar methods and achieved a superior performance. The obtained accuracy was over 95% in all conducted experiments. Based on the established results, it is reasonable to conclude that the proposed approach could be used to develop networks that can assist doctors in diagnostics and help in the early detection of brain tumors.

Highlights

IntroductionAs technology advances further every year, people are recognizing new means of solving certain problems with greater quality, precision, and efficiency
All methods are sorted according to their p value and compared with α/(k − i ), where k and i represent the degree of freedom and the algorithm number after sorting, according to the p value in ascending order, respectively
The introduced enhanced HHO (eHHO) algorithm was developed to target the observed drawbacks of the original method by incorporating the chaotic mechanism and a novel, quasi-reflexive learning replacement strategy that enhances both the exploitation and exploration, with only a small additional overhead in terms of its computational complexity and new control parameters

Summary

Introduction

As technology advances further every year, people are recognizing new means of solving certain problems with greater quality, precision, and efficiency. One of the technological domains that uncovered broad possibilities, and continues to do so, is artificial intelligence (AI). AI is not something that was revealed in the near past; it has existed for decades, but has only recently gained popularity among researchers and companies. The reason for this is the breakthrough in its processing power and storage capabilities, which increased the potential for more advanced AI applications. Most people are unaware that AI is influencing their life in multiple ways

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