# An Explainable Brain Tumor Detection Framework for MRI Analysis
## 1 Introduction  
Tumors have threatened human health for a long time. Malignant brain tumors, such as glioblastoma, have a very low survival rate and are highly correlated with the death of young patients with brain tumors [1]. Patients with brain tumors often undergo surgery, radiotherapy, chemotherapy, and other treatment methods, but distinguishing brain tumors from the brain parenchyma is difficult visually. This task needs to be carefully treated to locate and remove tumors through surgery. Medical staff usually make diagnosis first through image diagnosis technologies, such as electroencephalogram (EEG), computed tomography (CT), and nuclear magnetic resonance imaging (MRI), and assist in locating the diseased area and boundary through imaging software. computer-aided diagnosis systems (CADs) based on MRI can generate hundreds of images, which can be used to judge or classify the lesion area in detail [2]. CADs can detect and visualize various structures of the human brain, such as the blood-brain barrier and brain tumor boundary. However, medical researchers should pay attention to a series of problems, such as low contrast and clarity, while processing a large number of medical images due to the complexity of manual retrieval of MRI images and the need for classification. Whether these problems can be well-solved is related to doctors' treatment methods and decision-making plans. If these MRI sequences are automatically analyzed by machine learning, then medical professionals can more quickly confirm whether the tumor exists, then classify or analyze it if necessary. With the continuous development of artificial intelligence and interdisciplinary technologies, deep models have been increasingly applied to various medical image tasks, especially in brain science research [3]. Compared with natural scene images, medical image data samples are usually less, leading to many limitations in practical applications [4]. Deep models consider the fitting results of data input and output and often pay less attention to the information of middle layers. However, in the medical field, image classificationand segmentation based on MRI are often directly related to a series of important treatment methods, such as surgery, and have high risk. Medical professionals hold some distrust toward the output results due to the opacity of the black box structure [5]. Directly providing a segmentation or classification result may reduce the credibility on the prediction results. Explainable artificial intelligence (XAI) is necessary in the medical field because it can help people better understand deep models [6]. First, the diagnostic task is vulnerable to errors caused by subjective judgments [7]; second, explainability can increase the transparency of the system, improve the quality of evaluation, and provide help for accountability, fairness, and ethical issues [8]. Therefore, the application of XAI in medical images has practical significance. Through XAI, medical professionals have the opportunity to understand the principles behind medical findings and apply deep learning technology better [9]. The need for explainable tasks related to medical images is increasing. In the European Union, medical-related XAI has become a requirement of general data protection regulation (GDPR) [10]. Therefore, we propose an explainable framework for MRI images analysis of brain tumors to provide a judgment basis for medical researchers in diagnosis and treatment. Our contributions are as follows:  
1. Our framework includes two simple and efficient deep models, which can comprehensively analyze the MRI images of brain tumors and explain the classification results;  
2. We propose a brain tumor classification network based on re-parameterization. The model has the advantages of simplicity and efficiency and can fully capture space and channel information while considering classification and explainability tasks.