Enhancing brain tumor detection in MRI with a rotation invariant Vision Transformer.

IF 4.6 Q2 MATERIALS SCIENCE, BIOMATERIALS

ACS Applied Bio Materials Pub Date : 2024-06-18 eCollection Date: 2024-01-01 DOI:10.3389/fninf.2024.1414925

Palani Thanaraj Krishnan, Pradeep Krishnadoss, Mukund Khandelwal, Devansh Gupta, Anupoju Nihaal, T Sunil Kumar

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引用次数: 0

Abstract

Background: The Rotation Invariant Vision Transformer (RViT) is a novel deep learning model tailored for brain tumor classification using MRI scans.

Methods: RViT incorporates rotated patch embeddings to enhance the accuracy of brain tumor identification.

Results: 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.

Conclusion: 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.

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利用旋转不变视觉变换器增强核磁共振成像中的脑肿瘤检测。

背景旋转不变视觉变换器（RViT）是一种新型深度学习模型，专为使用核磁共振扫描进行脑肿瘤分类而定制：RViT结合了旋转补丁嵌入，以提高脑肿瘤识别的准确性：在 Kaggle 的脑肿瘤 MRI 数据集上进行的评估表明，RViT 的灵敏度 (1.0)、特异度 (0.975)、F1-分数 (0.984)、马修相关系数 (MCC) (0.972) 和总体准确度 (0.986) 均表现优异：RViT 优于标准视觉变换器模型和几种现有技术，突出了其在医学成像中的功效。研究证实，集成旋转补丁嵌入提高了模型处理不同方向的能力，这是肿瘤成像中的一个常见挑战。RViT 的专业架构和旋转不变性方法有望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。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

ACS Applied Bio Materials Chemistry-Chemistry (all)

CiteScore

9.40

自引率

2.10%

发文量

464