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Brain Tumor is a significant health challenge, with early, precise detection crucial for improving patient outcomes. Traditional diagnostics methods depend on expert radiologists, leading to subjectivity and time-intensive evaluations. In recent advancements, AI and DL have demonstrated remarkable medical imaging potential, yet many existing models are limited to binary classification, failing to distinguish between multiple tumor types. This limitation restricts their clinical applicability and effectiveness in comprehensive diagnosis. We propose a deep learning-based multi-class classification approach for brain tumor detection, leveraging EfficientNetV2 and Vision Transformer (ViT) models to address this challenge. EfficientNetV2, known for its optimized convolutional architecture, achieves high accuracy while maintaining computational efficiency, whereas ViT, a transformer-based model, effectively captures global contextual information in medical images. Our approach was evaluated on a brain tumor MRI dataset containing 7,023 images, divided into training and testing sets. EfficientNetV2 achieved an accuracy of 95% with a loss of 0.13, alongside an F1-score, precision, and recall of 0.96. In comparison, ViT attained 90% accuracy with a loss of 0.30 and an F1-score, precision, and recall of 0.89. The highest accuracy 96%, was achieved by the proposed model using the geometric mean ensemble learning technique. The results demonstrate that our proposed system outperforms many existing deep learning models, offering a robust solution for multi-class brain tumor classification. Our approach enhances diagnostic accuracy by integrating convolutional and transformer-based architectures, aiding radiologists in early and automated tumor detection. This research contributes to the advancement of AI-driven medical diagnostics and has the potential to improve clinical decision-making in brain tumor assessment.
Tariq et al. (Wed,) studied this question.