旋转不变视觉变换器（RViT）是一种专为使用 MRI 扫描进行脑肿瘤分类而定制的新型深度学习模型。RViT 结合了旋转补丁嵌入，以提高脑肿瘤识别的准确性。对 Kaggle 脑肿瘤 MRI 数据集的评估证明了 RViT 的卓越性能灵敏度 (1.0)、特异性 (0.975)、F1 分数 (0.984)、马修相关系数 (MCC) (0.972)，总体准确度为 0.986。RViT 优于标准 Vision Transformer 模型和多种现有技术，凸显了其功效在医学影像领域。该研究证实，集成旋转贴片嵌入可提高模型处理不同方向的能力，这是肿瘤成像中的常见挑战。 RViT 的专业架构和旋转不变性方法有可能增强当前的脑肿瘤检测方法，并扩展到其他复杂的成像任务。版权所有 © 2024 Krishnan、Krishnadoss、Khandelwal、Gupta、Nihaal 和 Kumar。

The Rotation Invariant Vision Transformer (RViT) is a novel deep learning model tailored for brain tumor classification using MRI scans.RViT incorporates rotated patch embeddings to enhance the accuracy of brain tumor identification.Evaluation on the Brain Tumor MRI Dataset from Kaggle demonstrates RViT's superior performance with sensitivity (1.0), specificity (0.975), F1-score (0.984), Matthew's Correlation Coefficient (MCC) (0.972), and an overall accuracy of 0.986.RViT outperforms the standard Vision Transformer model and several existing techniques, highlighting its efficacy in medical imaging. The study confirms that integrating rotational patch embeddings improves the model's capability to handle diverse orientations, a common challenge in tumor imaging. The specialized architecture and rotational invariance approach of RViT have the potential to enhance current methodologies for brain tumor detection and extend to other complex imaging tasks.Copyright © 2024 Krishnan, Krishnadoss, Khandelwal, Gupta, Nihaal and Kumar.