Brain Tumor Classification Based on Multi-Level Attention
- Resource Type
- Conference
- Authors
- Shi, Haoshu; Wu, Xiangping; Li, Xia
- Source
- 2023 IEEE 6th International Conference on Electronic Information and Communication Technology (ICEICT) Electronic Information and Communication Technology (ICEICT), 2023 IEEE 6th International Conference on. :264-269 Jul, 2023
- Subject
- Communication, Networking and Broadcast Technologies
Computing and Processing
Fields, Waves and Electromagnetics
Signal Processing and Analysis
Deep learning
Adaptation models
Magnetic resonance
Medical services
Brain modeling
Data augmentation
Data models
classification of brain tumor
deep learning
effective triplet attention
- Language
- ISSN
- 2836-7782
Over the previous few years, the incidence of brain tumors has been increasing, and it is now among the top 10 most prevalent tumors worldwide. Early detection and diagnosis of brain tumors can assist doctors in creating targeted treatment regimens. The method of deep learning to classify brain tumor magnetic resonance (MR) images can increase the speed and precision of diagnosis. This paper proposes a multi-level attention model to categorize brain tumors. This model uses EfficientNetV2 as the backbone. The Efficient Triplet Attention (ETA) module and the Pyramid Spilt Attention (PSA) module improve the model's capacity to retrieve meaningful information from MR images and decrease the impact of noisy information. The conclusion shows that our model can effectively increase the classification accuracy of brain tumors, with a rate of 98.53%.