## 2 Related Work
The achieved accuracy of 86.56% demonstrates that the presented method for brain tumor classification could successfully overcome CNNs. MR images are used to diagnose a brain tumor [29]. The use of CNN classification for automatic brain tumor detection was proposed by the authors. Small kernels were used to create deeper architecture. The neuron's weight is described as tiny. When compared to all other state-of-the-art methodologies, the experimental results demonstrate that CNN archives have a rate of 97.5% accuracy with little complexity. [PERSON] et al. adopted a Less Layered and Less Complex U-Net (LeU-Net) framework for brain tumor detection [30]. The LeU-Net idea was influenced by both the Le-Net and U-Net frameworks; however, it differs significantly from both architectural approaches. The performance of LeU-Net was compared to the existing basic CNN frameworks Le-Net, U-Net and VGG-16. Accuracy, precision, F-score, recall and specificity were used to assess CNN performance. The experiment was conducted on an MR dataset with cropped (removed unwanted area) and uncropped images. Moreover, the results were compared to all three models. The LeU-Net model has a much faster processing (simulation) time; training the network with 100 epochs achieved 98% accuracy on cropped images and achieved 94% accuracy on uncropped images, which took 252.36 s and 244.42 s, respectively. [PERSON] et al. proposed a new hybrid model for brain tumor identification and classification based on MR brain images [31], intending to assist doctors in the early diagnosis and classification of brain tumors with maximum accuracy and performance. The approach was developed using a hybrid deep CNN and a deep watershed auto-encoder (CNN-DWA) model. The technique can be broken down into six steps: input MR images, preprocessing with a filter and morphological operation, generating a matrix that represents MR brain images, using the hybrid CNN-DWA framework, brain tumor detection and classification and model performance evaluation. The model was validated using five databases: BRATS2012, BRATS2013, BRATS2014, ISLES-SISS 2015 and BRATS2015. Based on the RCNN technique [32], [PERSON] et al. developed a new framework for brain tumor classification and tumor type object recognition, tested using two publicly available datasets from Figshare and Kaggle. The goal was to design a basic framework that would allow the classic RCNN framework to run faster. Glioma and healthy tumor MRI images were initially classified using a two-channel CNN. Later, a feature extractor in an RCNN was used to locate tumor locations in a glioma MRI sample categorized from a previous stage using the same framework. Bounding boxes were used to define the tumor region. Meningioma and pituitary tumors are two more malignancies that have been treated with this method. The proposed method achieved an average confidence level of 98.83% for two-class tumor classification, i.e., meningioma and pituitary tumors. Existing works on brain tumor detection and classification have some limitations. Most of the approaches are validated with the figshare dataset, which is an imbalanced dataset and affects the performances of classification approaches. Hence, there is a need to validate brain tumor classification approaches on another balanced dataset. ML, in its traditional form, necessitates domain knowledge and experience. Manual feature extraction necessitates time and effort, reducing the system's efficiency. On the other hand, employing DL, particularly CNN, in medical imaging is challenging, as it requires a significant amount of data for training. In contrast, deep TL-based algorithms can avoid these drawbacks by using automatic feature extraction and robust classification applications based on convolutional layers. This study proposes an automatic classification system for multiclass brain tumor MR images, which is a more complex and difficult assignment than simple binary classification.