AN IN-DEPTH ANALYSIS OF ATTENTION MECHANISMS IN BRAIN TUMOR DETECTION: EXPLORING DIVERSE ATTENTION MODALITIES

Abstract

Abstract. Brain tumors pose a significant challenge in medical diagnosis, requiring accurate and timely detection for optimal patient treatment. While traditional imaging methods have their advantages, they lack the necessary sensitivity and specificity, prompting the investigation of advanced computational approaches. This research examines the effectiveness of different attention mechanisms in improving the detection of brain tumors to enhance diagnostic accuracy, while also addressing challenges like the need for diverse datasets and model interpretability. The Visual Transformer model achieves an impressive accuracy of 96%, while the Multi-head Attention model shows slightly lower precision and recall but still maintains a respectable accuracy of 93.6%. The Luong Attention model performs moderately well at 89%, and both the Additive Attention and Self-Attention models achieve around 93-94% accuracy across the dataset. These results highlight the potential of the Visual Transformer model to transform the field of brain tumor detection. Nevertheless, further investigation and optimization of alternative models are essential. Attention mechanisms offer a promising approach to improving brain tumor detection, with the potential to have a significant impact on patient care and outcomes. Continued research in this area has the potential to enhance diagnostic capabilities and ultimately improve patient outcomes in the field of brain tumor detection.