Enhancing a Hyper-parameter Tuning of Convolutional Neural Network Model for Brain Tumor Classification using Whale Optimization and Grey Wolf Optimizer
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Date
2024
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Publisher
Newcastle University
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
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Keywords
Brain Tumor Classification, Convolutional Neural Network, Whale Optimization Algorithm, Grey Wolf Optimizer, MRI Image, Deep Learning