Brain tumor classification in VIT-B/16 based on relative position encoding and residual MLP

Brain tumors pose a significant threat to health, and their early detection and classification are crucial. Currently, the diagnosis heavily relies on pathologists conducting time-consuming morphological examinations of brain images, leading to subjective outcomes and potential misdiagnoses. In resp...

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Bibliographic Details
Published in:PloS one Vol. 19; no. 7; p. e0298102
Main Authors: Hong, Shuang, Wu, Jin, Zhu, Lei, Chen, Weijie
Format: Journal Article
Language:English
Published: United States Public Library of Science 02.07.2024
Subjects:
Accuracy
Algorithms
Artificial intelligence
Biology and Life Sciences
Blended learning
Brain
Brain cancer
Brain Neoplasms - classification
Brain Neoplasms - diagnostic imaging
Brain Neoplasms - pathology
Brain research
Brain tumors
Classification
Coding
Computer and Information Sciences
Convergence
COVID-19
Datasets
Deep learning
Effectiveness
Efficiency
Health aspects
Health risks
Humans
Image enhancement
Image processing
Magnetic resonance imaging
Malaria
Medical imaging
Medical imaging equipment
Medical research
Medicine and Health Sciences
Methods
Multilayer perceptrons
Multilayers
Neural networks
Neural Networks, Computer
Research and Analysis Methods
Social Sciences
Tumors
ISSN:1932-6203, 1932-6203
Online Access:Get full text
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Summary:Brain tumors pose a significant threat to health, and their early detection and classification are crucial. Currently, the diagnosis heavily relies on pathologists conducting time-consuming morphological examinations of brain images, leading to subjective outcomes and potential misdiagnoses. In response to these challenges, this study proposes an improved Vision Transformer-based algorithm for human brain tumor classification. To overcome the limitations of small existing datasets, Homomorphic Filtering, Channels Contrast Limited Adaptive Histogram Equalization, and Unsharp Masking techniques are applied to enrich dataset images, enhancing information and improving model generalization. Addressing the limitation of the Vision Transformer’s self-attention structure in capturing input token sequences, a novel relative position encoding method is employed to enhance the overall predictive capabilities of the model. Furthermore, the introduction of residual structures in the Multi-Layer Perceptron tackles convergence degradation during training, leading to faster convergence and enhanced algorithm accuracy. Finally, this study comprehensively analyzes the network model’s performance on validation sets in terms of accuracy, precision, and recall. Experimental results demonstrate that the proposed model achieves a classification accuracy of 91.36% on an augmented open-source brain tumor dataset, surpassing the original VIT-B/16 accuracy by 5.54%. This validates the effectiveness of the proposed approach in brain tumor classification, offering potential reference for clinical diagnoses by medical practitioners.
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Competing Interests: The authors have declared that no competing interests exist.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0298102