## 5 Conclusions  
In this article, a GAT-based transformer called GATransformer employs the attention mechanism, GAT, and Transformer to detect and sustain patient care neural network channels without medical competence. Channel attention improves model representation by extracting more profound attributes from weight-channel relationships and facilitates channel pruning, while Transformer models can learn complex spatial connections. Integrating these aspects reduces model size and improves computer efficiency while maintaining model performance. The FigShare and Kaggle datasets were used to initially train the proposed model, whereas BraTS2019 and BraTS2020 datasets were employed to cross-validate the proposed model. Ground-truth images from these two datasets were employed to validate the proposed model. The GATransformer model's resource-intensive computational complexity and training time limit its real-time deployment. Even with SMOTE for class balancing, utilizing Kaggle and FigShare datasets increases the overfitting risk. Additionally, model performance depends on high-quality MRI scans, making it sensitive to noise and artifacts. While explainable attention processes are used, deep learning models, especially those using GAT and Transformers, remain \"black boxes\" and pose interpretability issues. Medical AI explainability is difficult, but our GATransformer model classifies brain tumors well. Healthcare is vital, so we highlight the human-in-the-loop (HITL) strategy to use AI as a decision-support tool rather than replacing doctors. HITL works in radiology and diagnostics, where AI aids but experts approve. Our model improves efficiency and clinical application trust by following this paradigm. More efficient and accurate pruning algorithms and channel pruning in more extensive and more complicated networks should be studied in the future. Channel pruning is promising and may help construct more efficient and powerful deep neural networks.  
Conceptualization, I.M.N.; Methodology, R.D.; Software, S.T.; Validation, S.T., I.M.N. and R.D.; Formal analysis, S.T. and I.M.N.; Investigation, S.T.; Resources, I.M.N.; Writing--original draft, S.T. and I.M.N.; Writing--review and editing, R.D.; Visualization, S.T.; Supervision, R.D. All authors have read and agreed to the published version of the manuscript.  
Figure 15: Analysis of the proposed model—training dataset: Kaggle; testing dataset: BraTS2019 and BraTS2020 datasets.  
**Funding:** This research received no external funding.  
**Data Availability Statement:** This study uses the following openly available datasets: Brain Tumor MRI Dataset (Kaggle Dataset) [25], Brain Tumor Dataset (FigShare Dataset) [26], BraTS2019 dataset ([[https://www.kaggle.com/datasets/debbratachakraborty/brats2019-dataset](https://www.kaggle.com/datasets/debbratachakraborty/brats2019-dataset)]([https://www.kaggle.com/datasets/debbratachakraborty/brats2019-dataset](https://www.kaggle.com/datasets/debbratachakraborty/brats2019-dataset)), accessed on 24 October 2024) and BraTS2020 datasets ([[https://www.kaggle.com/datasets/awsaf49/brats20-dataset-training-validation](https://www.kaggle.com/datasets/awsaf49/brats20-dataset-training-validation)]([https://www.kaggle.com/datasets/awsaf49/brats20-dataset-training-validation](https://www.kaggle.com/datasets/awsaf49/brats20-dataset-training-validation)), accessed on 24 October 2024).  
**Conflicts of Interest:** The authors declare no conflicts of interest.