## 1 Introduction
Additionally, there are a lot of irregularities in the sizes and positions of brain tumors, which makes the natural understanding of brain tumors problematic. Generally, for the classification of brain tumors, T1-weighted contrast-enhanced (with gadolinium-enhanced) MRI images (T1c) are used because tumors are considerably better visualized on T1c due to the stimulation of 0.150 mMol/kg of contrast material (gadolinium) in patients [17]. Diffusion-weighted imaging (DWI) is also considered vital for detecting brain tumorsbecause it can visualize restrictions to the free diffusion of water caused by tissue microstructures [18]. The different features extracted from MRI images are key sources for tumor classification. DL makes predictions and decisions on data by learning data representation. DL practices are most widely used for medical imaging classification. However, DL-based methods have shown satisfactory results in various applications across a wide range of domains in various fields [19; 20; 21; 22]. However, DL approaches are starving data approaches, i.e., they require a lot of training data. Recently, DL approaches, particularly the CNN model, have been attracting more and more attention. CNN outperforms other classifiers on larger datasets, such as ImageNet, consisting of millions of images. However, it is challenging to employ CNNs in the field of medical imaging. Firstly, medical image datasets contain limited data because expert radiologists are required to label the dataset's images, which is a tedious and time-consuming task. Secondly, CNN training is difficult for a small dataset because of overfitting. Thirdly, hyperparameters of CNN classifiers need to be adjusted to achieve better performance that requires domain expertise. Therefore, using pre-trained models on TL and fine tuning are viable solutions to address these challenges. In TL approaches, DL models are trained on a large dataset (base dataset) and transfer learned knowledge to the target dataset (small dataset) [23]. This paper proposes an automatic brain tumor classification approach intended for three-class classification. Several approaches utilize manually defined tumor regions to detect and classify brain tumors, preventing them from being fully automated [1; 2; 19]. However, the proposed new approach does not involve any segmentation or feature extraction and selection in the pre-processing step, in contrast to some previous methods [1; 2; 19], which require prior segmentation and feature extraction of tumors from MRI images. We use a standard Kaggle brain tumor classification (MRI) dataset, including three types of brain tumors: meningioma, pituitary and glioma. We perform extensive experiments based on this dataset to compare the performance of nine DL models for the classification of brain tumor MRI images using TL. We used Inceptionresnetv2, Inceptionv3, Xception, Resnet18, Resnet50, Resnet101, Shufflenet, Densenet201 and Mobilenetv2 for the automatic detection and classification of brain tumors using a fine-grained classification approach. Furthermore, several approaches utilize the manually defined tumor regions _to detect and classify_ brain tumors that prevent them from being fully automated. The aim is to identify the most effective and efficient deep TL model for brain tumor classification. We report the overall accuracy, precision, recall, f-measure and elapsed time of the nine pre-trained frameworks in this paper. The key contributions of our research include the following:  
* Proposing a DL-based framework for automatically detecting and classifying brain tumors into meningioma, pituitary and glioma tumors. * Analyzing and validating the TL concept for nine different deep neural networks. * Analyzing the performance of each TL model in classifying brain MRI images correctly and efficiently. * Comparing the performance of TL approaches with hybrid approaches (DL + SVM). The remainder of the paper is organized into the following subsections. Section 2 provides details about the existing literature on brain MRI classification.