VITALT: a robust and efficient brain tumor detection system using vision transformer with attention and linear transformation

Poornam, S.; Angelina, J. Jane Rubel

doi:10.1007/s00521-023-09306-1

Cited by 1 publication

(2 citation statements)

References 34 publications

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“…Pioneering studies have demonstrated the potential of ViTs in this domain. Poornam and Angelina (2024) Addressing data scarcity and variance, a key challenge in medical imaging, Haque et al (2023) proposed a novel approach integrating DCGAN-based data augmentation with ViTs, demonstrating the transformative potential of combining GANs and ViTs for enhanced diagnostic accuracy. Bhimavarapu et al (2024) developed a system that couples an improved unsupervised clustering approach with a machine learning classifier, aiming to enhance the accuracy of brain tumor detection and categorization.…”

Section: Related Workmentioning

confidence: 99%

“…Pioneering studies have demonstrated the potential of ViTs in this domain. Poornam and Angelina ( 2024 ) introduced VITALT, an innovative system that combines ViTs with attention and linear transformation mechanisms for brain tumor detection, showcasing superior performance in classifying tumors from MRI samples and setting a new benchmark for future research. Jahangir et al ( 2023 ) compared the effectiveness of ViTs and CNN-based classifiers, highlighting the unique advantages of ViTs in capturing intricate patterns and features from medical images.…”

Section: Related Workmentioning

confidence: 99%

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

Krishnan,

Krishnadoss,

Khandelwal

et al. 2024

Front. Neuroinform.

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BackgroundThe Rotation Invariant Vision Transformer (RViT) is a novel deep learning model tailored for brain tumor classification using MRI scans.MethodsRViT incorporates rotated patch embeddings to enhance the accuracy of brain tumor identification.ResultsEvaluation 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.ConclusionRViT 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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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%