# An Explainable Brain Tumor Detection Framework for MRI Analysis
## 2 Related Work  
Explainability based on deep models can be divided into input-dependent explanations (instance-level explanations) and input-independent explanations (model-level explanations). The former can find and explain the features that have the greatest impact on the prediction results, while the latter can directly explain the model without considering the network input. In medical imaging, with the clarity of segmentation and classification, explainability of these tasks usually adopt instance-level explanations, of which the more widely used are perturbation-based methods and gradient-based methods. In the early stage of medical image explainability work, perturbation-based methods are often used. These methods can study the network by observing output changes under different input disturbances. In the medical field, disturbances can include various forms, such as shape and occlusion [11; 12]. However, since the running time depends on the number of input features, more computing resources and time are often needed to achieve better results, so more consideration should be given to the choice between accuracy and explainability. In gradient-based methods, gradient is the approximate value of the importance of the input feature and is highly related to model parameters. Similar models often have the advantage of post-processing. Explainability can be independent of the model training process, avoiding the balance between accuracy and computational loss. Gradients used in XAI include integral gradient (IG) [13], vanilla gradient (VG) [14], and guided backpropagation (GB) [15]. In recent years, researchers have increasingly used extension methods based on class activation mapping (CAM) [16], such as Grad-CAM [17] and Guided Grad-CAM [17]. These improved methods do not change the model structure nor need to retrain the model. In the application of deep models in the medical field, researchers mainly use target detection networks based on deep learning, such as U-Net [18], VGG [19], ResNet [20], and GoogLeNet [21], which have important contributions to the diagnosis of a series of diseases. [PERSON] et al. [22] used Fully Convolutional Networks in liver tumor image classification and found that it improved the tumor localization effect. [PERSON] et al. [23] proposed an ACAE model that can be used to classify different parts of the spine and quickly detect the suspected lesion areas. [PERSON] et al. [24] used a variety of networks to diagnose and identify COVID-19 on chest X-ray datasets. [PERSON] et al. [25; 26] proposed a variety of brain tumor classification models based on deep learning. Based on the pre-trained VGG-like model, tumor classification is performed through migration learning. In recent years, researchers have used XAI to comprehensively evaluate and explain model results. [PERSON] et al. [27] used 3D convolutional neural networks (CNNs) for the classification and visual explainability of [PERSON]'s disease. [PERSON] et al. [28] used GB to analyze the explainability of colon polyps. [PERSON] et al. [29] added explainability method based on Grad-CAM to the segmentation task of 2D glioma. [PERSON] et al. [30] applied similar methods to 3D images. [PERSON] et al. [31] used Grad-CAM to explain the brain tumor segmentation task. [PERSON] et al. [32] found that CAM-based methods are better in classification tasks through the sanity check. [PERSON] et al. [33] proposed an explainable method combining global and local information for tumor segmentation. They used GB and CAM in brain tumors detection. The experimental results show that GB can distinguish important areas rather than categories, and CAM works well in both tasks. [PERSON] et al. [34] used GoogLeNet and ResNet to detect malaria, diabetic retinopathy, brain tumors, and tuberculosis in different imaging modalities. They visualized the class activation mappings to enhance the understanding of these deep networks. Although XAI has been widely used in natural image tasks, it is insufficient to be applied to medical tasks [9].