Work

Combining Local and Global Feature Extraction for Brain Tumor Classification: A Vision Transformer and iResNet Hybrid Model

Year: 2024

Type: article

Abstract: Early diagnosis of brain tumors is crucial for effective treatment and patient prognosis. Traditional Convolutional Neural Networks (CNNs) have shown promise in medical imaging but have limitations in capturing long-range dependencies and contextual information. Vision Transformers (ViTs) address these limitations by leveraging self-attention mechanisms to capture both local and global features. This study aims to enhance brain tumor classification by integrating an improved ResNet (iResNet) architecture with a ViT, creating a robust hybrid model that combines the local feature extraction capabilities of iResNet with the global feature extraction strengths of ViTs. This integration results in a significant improvement in classification accuracy, achieving an overall accuracy of 99.2%, outperforming established models such as InceptionV3, ResNet, and DenseNet. High precision, recall, and F1 scores were observed across all tumor classes, demonstrating the model's robustness and reliability. The significance of the proposed method lies in its ability to effectively capture both local and global features, leading to superior performance in brain tumor classification. This approach offers a powerful tool for clinical decision-making, improving early detection and treatment planning, ultimately contributing to better patient outcomes.

Source: Engineering Technology & Applied Science Research

DOI: 10.48084/etasr.8271

Author: Amar Y. Jaffar

Institution: Umm al-Qura University

Language: English

Topic: Brain Tumor Detection and Classification

Subfield: Neurology

Field: Neuroscience

Domain: Life Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $431