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Published on: August 16, 2020
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Brain tumor classification in VIT-B/16 based on relative position encoding and residual MLP
Shuang Hong1, Jin Wu1, Lei Zhu1
1School of Information Science and Engineering, Wuhan University of Science and Technology, Wuhan, Hubei, China.
Plos One
|July 2, 2024
Summary
This study introduces an enhanced Vision Transformer algorithm for accurate brain tumor classification. The improved model achieves 91.36% accuracy, aiding medical practitioners in early diagnosis.
Area of Science:
- Medical Imaging
- Artificial Intelligence
- Computer Vision
Background:
- Brain tumor diagnosis relies on time-consuming, subjective manual image analysis.
- Existing methods face challenges with small datasets and limitations in deep learning models.
- Accurate and early classification of brain tumors is critical for patient outcomes.
Purpose of the Study:
- To develop an improved Vision Transformer (VIT) algorithm for enhanced brain tumor classification.
- To address dataset limitations and improve the model's ability to capture spatial information.
- To increase the accuracy and efficiency of automated brain tumor diagnosis.
Main Methods:
- Applied data augmentation techniques: Homomorphic Filtering, Channels Contrast Limited Adaptive Histogram Equalization, and Unsharp Masking.
- Introduced a novel relative position encoding method to enhance Vision Transformer's self-attention mechanism.
- Incorporated residual structures within the Multi-Layer Perceptron to improve training convergence and accuracy.
Main Results:
- The proposed algorithm achieved a classification accuracy of 91.36% on an augmented dataset.
- Demonstrated a significant improvement of 5.54% over the original VIT-B/16 model.
- Exhibited enhanced precision and recall, validating the model's effectiveness.
Conclusions:
- The enhanced Vision Transformer algorithm significantly improves brain tumor classification accuracy.
- Data augmentation and architectural modifications effectively address model limitations.
- The developed model shows promise as a valuable tool for clinical decision support in neuro-oncology.
