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

IF 2.5 4区医学 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY Frontiers in Neuroinformatics 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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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

Frontiers in Neuroinformatics MATHEMATICAL & COMPUTATIONAL BIOLOGY-NEUROSCIENCES

CiteScore

4.80

自引率

5.70%

发文量

132

审稿时长

14 weeks

期刊介绍： Frontiers in Neuroinformatics publishes rigorously peer-reviewed research on the development and implementation of numerical/computational models and analytical tools used to share, integrate and analyze experimental data and advance theories of the nervous system functions. Specialty Chief Editors Jan G. Bjaalie at the University of Oslo and Sean L. Hill at the École Polytechnique Fédérale de Lausanne are supported by an outstanding Editorial Board of international experts. This multidisciplinary open-access journal is at the forefront of disseminating and communicating scientific knowledge and impactful discoveries to researchers, academics and the public worldwide. Neuroscience is being propelled into the information age as the volume of information explodes, demanding organization and synthesis. Novel synthesis approaches are opening up a new dimension for the exploration of the components of brain elements and systems and the vast number of variables that underlie their functions. Neural data is highly heterogeneous with complex inter-relations across multiple levels, driving the need for innovative organizing and synthesizing approaches from genes to cognition, and covering a range of species and disease states. Frontiers in Neuroinformatics therefore welcomes submissions on existing neuroscience databases, development of data and knowledge bases for all levels of neuroscience, applications and technologies that can facilitate data sharing (interoperability, formats, terminologies, and ontologies), and novel tools for data acquisition, analyses, visualization, and dissemination of nervous system data. Our journal welcomes submissions on new tools (software and hardware) that support brain modeling, and the merging of neuroscience databases with brain models used for simulation and visualization.