Enhancing a Hyper-parameter Tuning of Convolutional Neural Network Model for Brain Tumor Classification using Whale Optimization and Grey Wolf Optimizer

Abstract

Brain tumors represent a global health issue, with about 11 new cases per 100,000 people annually. Therefore, it is crucial to develop faster and more accurate diagnostic solutions. This study develops and evaluates a convolutional neural network (CNN) model optimized using the Whale Optimization Algorithm (WOA) and Grey Wolf Optimizer (GWO) for classifying brain tumors. To achieve that, this work involved collecting and preprocessing an MRI brain tumor dataset, followed by building and training CNN models. Hyperparameters were optimized using WOA and GWO, and the performance of these optimized mod- els was compared against a non-optimized CNN. The WOA-optimized CNN outperformed both the non-optimized and GWO-optimized mod- els, achieving an accuracy of 93.4% and demonstrating superior general- ization across different classes. This study underscores the effectiveness of WOA in enhancing CNN models for medical image classification, of- fering promising approaches to enhancing the accuracy and reliability of brain tumor classification