# An Explainable Brain Tumor Detection Framework for MRI Analysis
## 4 Results and Discussion
### Discussion  
XAI has great potential for widespread application in the healthcare industry. It can help doctors make more informed diagnostic and treatment decisions, avoid bias and data misuse, and increase the credibility of artificial intelligence in healthcare. One important application scenario for explainability is smart terminals. Whether for medical professionals or patients, smart terminal devices need to make quick and accurate decisions. Therefore, explainable maps based on deep learning models need to minimize computation and inference time while ensuring accuracy to meet real-time requirements. In this scenario, the inference speed of deep models is very important. In order to better verify the role of each part in our model, we performed ablation analysis on the BraTS dataset. We trained with the same loss function and input. In our tests, when changing the overall structure of the model to a 3 \(\times\) 3 CNNs similar to VGG, the model accuracy was 87%, while using the VGG16 model had an accuracy of about 86%, with little difference between them. After removing the atrous convolution layers from our network, the model accuracy was about 94.5%. Therefore, we found that RepOpt contributed about 8% to accuracy and atrous convolution layers contributed about 1% to \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Method** & **DataSet** & **Accuracy** \\ \hline [PERSON] et al., 2018 [42] & 2D CNNS & BraTS 2017 & 90.87\% \\ [PERSON] et al., 2020 [26] & VGG and EML & BraTS 2018 & 92.5\% \\ [PERSON] et al., 2021 [43] & 3D CNNS & BraTS 2018 & 92.67\% \\ [PERSON] et al., 2022 [44] & FCM-IWOA-RBNN & BraTS 2018 & 96\% \\ Our Model & RepOpt & BraTS 2018 & 95.46\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Quantitative results of classification model on BraTS dataset. accuracy. This is consistent with our expectations. RepOpt provides a feature extraction method similar to multi-branch models, effectively improving model accuracy. The atrous convolution layers are mainly used to provide a better receptive field and can generate more accurate heatmaps. At the same time, we tested inference speed for these models and found that they were not significantly different at about 920 samples/second. This shows that although our model has improved accuracy, the speed of our model during the inference stage is not much different from that of simple models. Although the explainability of medical images can help medical researchers better understand the deep model results, the current exploration and work of explainability are insufficient. First, explainability lacks datasets and evaluation indicators in medical images field. The explainability of medical images is often accompanied by segmentation or classification models, which cannot be trained independently. The BraTS dataset used in this paper is one of the few datasets that can provide segmentation GT and classification labels at the same time. At present, explainability is usually applied to image classification tasks, because explainability can be seen as a more intuitive visualization output. However, the explainability is similar to the segmentation result, which is a pixel-by-pixel classification result. The indicators of explainability and classification model are not equal. Although some scholars have made useful explorations on evaluation indicators [33], there are no recognized evaluation indicators for evaluating explainability in brain tumor diagnosis. Secondly, deep models depend on gradient information and input images. Therefore, the  
Figure 7: Comparison of brain glioma segmentation and explainability. models need to balance performance and computational loss, especially when dealing with high-dimensional data, such as 3D data. In addition, an important application of explainability is to shift from professional-oriented to user-oriented, and generate automatic detection reports in combination with natural language processing to provide help for end-to-end medical automatic solutions.