Brain Tumor Classification Using Deep Ensemble Learning Based on Voting Technique
DOI:
https://doi.org/10.59222/ustjet.4.1.4Keywords:
Brain tumor classification, deep learning, ensemble learning, convolutional neural network, voting technique, magnetic resonance imagingAbstract
Brain tumor classification is a crucial process in the medical diagnosis field of brain lesions for obtaining a correct diagnosis and then beginning treatment planning. In this study, an ensemble learning approach using a VGG19 model is proposed to achieve high classification accuracy. Three VGG19 models with varying parameters are trained independently. The predictions of the three models are combined using ensemble learning based on the voting technique. The models were trained on a comprehensive dataset of brain MRI scans containing 7,041 MRI images, split into 80% training and 20% testing sets. The raw train dataset is resized and then fed to the three VGG19 models individually. The entire test dataset is preprocessed with sharpening techniques to enhance the details of brain images. The proposed approach achieved an impressive accuracy of 99.33 % on the test dataset, surpassing state-of-the-art methods in brain tumor classification. Furthermore, the proposed approach obtained high precision, specificity, recall, and F1-score, showing its strength. The results demonstrate the effectiveness of the proposed approach compared to individual models, with significant improvements observed through ensemble learning. This study contributes to the field of medical diagnosis by providing an accurate framework for auto-brain tumor classification.
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