## 1 Introduction  
Masses created by cells growing improperly in the brain are called brain tumors [1]. They occur in various sizes and shapes as benign and malignant. Benign tumors grow more slowly and cause less damage to brain tissues. Malignant tumors grow faster than benign ones and cause more damage, creating an extremely dangerous situation for human life. Although the cause of brain tumors is unknown, environmental and genetic factors, and some types of radiation, are effective in tumor formation. Along with these two forms of brain tumor, other common tumors are pituitary, glioma, and meningioma. A typically benign tumor called a meningioma forms on the brain and spinal cord's meninges [2]. Surgery or radiation therapy can be used to treat this slow-growing tumor that gradually presses against the surrounding tissues. Pituitary tumors are often benign and arise from aberrant pituitary gland cell proliferation. Hormone deficits and visual issues may result from it [3]. The most prevalent malignant tumor is glioma. Approximately one-third of brain malignancies are caused by it. The prognosis for glioblastoma, the late stage of glioma, is dismal and ranges from one to one and a half years after diagnosis [4]. Benign or malignant tumors grow over time and create high pressure inside the skull. The brain tissues are damaged by this pressure effect. In order to minimize this damage and increase the patient's survival rate, the brain tumor should be detected as early and accurately as possible. In addition to the expertise of radiologists, imaging techniques are also of great importance in tumor detection. There are modalities such as computedtomography (CT), positron emission tomography (PET), X-rays and magnetic resonance imaging (MRI) to diagnose brain tumors [5]. MRI is more useful than other methods and is one of the most efficient techniques to obtain detailed information about the anatomy, location, shape, and size of the tumor [6]. Brain tumors are a health problem with high mortality rates worldwide. Early detection of tumors, making the correct diagnosis and starting appropriate treatment as soon as possible are very important for the patient's life expectancy and quality. Traditional methods used to diagnose brain tumors are based on manual examination of imaging data by radiologists. However, since these examinations depend on the expertise of the radiologist, they are open to error, require intensive labor and are time-consuming processes [7]. Performing such a process automatically and as quickly and accurately as possible will directly affect a patient's life with the advantage of early diagnosis. The risk of spreading tumors that are detected at early stages can be prevented, the damage to brain tissues can be reduced and, since they may be smaller in size, they can be treated at a higher rate, with treatment methods such as radiotherapy, chemotherapy or surgical intervention. This reduces mortality rates and significantly improves patient life. Machine learning (ML) and deep learning (DL) methods are utilized to automate the process of manually analyzing medical images. Machine learning methods use classified data. They classify new data of unknown class by learning features from labeled datasets. Classification can be undertaken on many datasets, including medical images, and using many methods [8], such as support vector machines (SVMs), k-nearest neighbor (KNN), naive Bayes (NB), decision tree (DT) and random forest (RF). Deep learning methods automatically extract complex features from larger data using artificial neural networks. Data are processed through many layers and classification is achieved with higher accuracy rates [9; 10; 11]. While ML and DL models have shown notable progress in the highly rapid and accurate detection of brain tumors [12], their black box nature creates a lack of confidence in the results. As the explanations for the models' decisions are unclear, it is difficult for medical professionals to trust the classification outputs of the models.