Hybrid Aquila optimizer-Harris Hawks optimization for CNN hyperparameter tuning in brain tumor classification

Magnetic resonance imaging (MRI) is hard to categorize properly in terms of interclass similarity, there is data imbalance, and sensitive clinical decision-making: but the performance of convolutional neural networks (CNNs) highly relies on effective, yet computationally costly, hyperparameter tuning. To find solutions to such issues, the given paper proposes a hybrid solution to the problems of the Aquila Optimizer and Harris Hawks Optimization, i.e., Aquila Optimizer-Harris Hawks Optimization (AO-HHO) framework, to integrate the positive qualities of extremely good global exploration of the Aquila Optimizer and the good local exploitation process of a Harris Hawks Optimization to achieve balanced and robust CNN hyperparameter optimization. On a publicly accessible dataset of 7, 023 brain MRI images divided into glioma, meningioma, pituitary tumor, and non-tumor, the proposed algorithm has been tested on with fine-tuning critical hyperparameters, such as learning rate, batch size, number of filters, dropout rate, and optimizer type. The rate of accuracy, precision, recall and F1-score of the AO-HHO-tuned CNN is invariably high than the conventional metaheuristic algorithms, including the Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Whale Optimization Algorithm (WOA) that are approximately 78-83. The proposed method also helps in reducing the cost of computing. It takes only 77.85 s to train, while the baseline optimizers take more than 300 s. This shows that AO-HHO is a reliable, accurate, and computationally efficient framework that can be used for medical imaging decision-support applications that need to be done in real time and with limited resources.