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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2410.21872 |
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| _version_ | 1866917829461147648 |
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| author | Lai, Yinyi Cao, Anbo Gao, Yuan Shang, Jiaqi Li, Zongyu Guo, Jia |
| author_facet | Lai, Yinyi Cao, Anbo Gao, Yuan Shang, Jiaqi Li, Zongyu Guo, Jia |
| contents | Early and accurate diagnosis of brain tumors is crucial for improving patient survival rates. However, the detection and classification of brain tumors are challenging due to their diverse types and complex morphological characteristics. This study investigates the application of pre-trained models for brain tumor classification, with a particular focus on deploying the Mamba model. We fine-tuned several mainstream transfer learning models and applied them to the multi-class classification of brain tumors. By comparing these models to those trained from scratch, we demonstrated the significant advantages of transfer learning, especially in the medical imaging field, where annotated data is often limited. Notably, we introduced the Vision Mamba (Vim), a novel network architecture, and applied it for the first time in brain tumor classification, achieving exceptional classification accuracy. Experimental results indicate that the Vim model achieved 100% classification accuracy on an independent test set, emphasizing its potential for tumor classification tasks. These findings underscore the effectiveness of transfer learning in brain tumor classification and reveal that, compared to existing state-of-the-art models, the Vim model is lightweight, efficient, and highly accurate, offering a new perspective for clinical applications. Furthermore, the framework proposed in this study for brain tumor classification, based on transfer learning and the Vision Mamba model, is broadly applicable to other medical imaging classification problems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_21872 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Advancing Efficient Brain Tumor Multi-Class Classification -- New Insights from the Vision Mamba Model in Transfer Learning Lai, Yinyi Cao, Anbo Gao, Yuan Shang, Jiaqi Li, Zongyu Guo, Jia Computer Vision and Pattern Recognition Artificial Intelligence Early and accurate diagnosis of brain tumors is crucial for improving patient survival rates. However, the detection and classification of brain tumors are challenging due to their diverse types and complex morphological characteristics. This study investigates the application of pre-trained models for brain tumor classification, with a particular focus on deploying the Mamba model. We fine-tuned several mainstream transfer learning models and applied them to the multi-class classification of brain tumors. By comparing these models to those trained from scratch, we demonstrated the significant advantages of transfer learning, especially in the medical imaging field, where annotated data is often limited. Notably, we introduced the Vision Mamba (Vim), a novel network architecture, and applied it for the first time in brain tumor classification, achieving exceptional classification accuracy. Experimental results indicate that the Vim model achieved 100% classification accuracy on an independent test set, emphasizing its potential for tumor classification tasks. These findings underscore the effectiveness of transfer learning in brain tumor classification and reveal that, compared to existing state-of-the-art models, the Vim model is lightweight, efficient, and highly accurate, offering a new perspective for clinical applications. Furthermore, the framework proposed in this study for brain tumor classification, based on transfer learning and the Vision Mamba model, is broadly applicable to other medical imaging classification problems. |
| title | Advancing Efficient Brain Tumor Multi-Class Classification -- New Insights from the Vision Mamba Model in Transfer Learning |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2410.21872 |