## 2 Literature Review
DenseNet201 surpasses all other models in terms of accuracy, with a training accuracy of 97.48% and a validation accuracy of 96.42%. Similarly, [PERSON] et al. proposed in [27] a transfer learning approach using the InceptionV3 and DenseNet201 models to categorize brain tumors on a publicly accessible dataset. Data augmentation strategies can be employed to obtain accurate classification results by enhancing the dataset and mitigating overfitting. A deep convolutional neural network named \"Brain-Deep Net\" uses six closely coupled convolutional layers to retrieve information from thick layers. The proposed model was shown to have a classification accuracy of 96.3% for the three most popular types of brain tumors. glioma, meningioma, and pituitary. In [28], [PERSON] et al. presented the most cutting-edge and powerful deep learning techniques. These techniques were separated into two groups: convolutional neural network (CNN) approaches and vision transformer (ViT) approaches. Both groups were ranked according to their effectiveness. Additionally, the study uses ensemble learning approaches to increase the accuracy of model outputs and data augmentation strategies to improve data variety. The performance of the models in terms of classification was enhanced by the discovery of high-performing models through the utilization of ensemble learning methodologies, which ultimately results in an accuracy of 91.76%. In medical image analysis, small datasets are a widespread problem. Most computer vision works might benefit from extra data, and data augmentation is one method frequently employed to improve computer vision system performance. In [29], [PERSON] et al. proposed the classification of the handwritten digit as one of the first uses of data augmentation. Data augmentation techniques on the ImageNet dataset improved picture classification. The proposed strategy aims to expand the dataset size. The authors conducted the studies by randomly cutting portions of the original photos, flipping them horizontally, and altering the pixel intensity. In [30] [PERSON] et al., proposed a model that could detect the tumor using a support vector machine (SVM) after image segmentation with Particle Swarm Optimization (PSO) and Watershed algorithms, Principal Component Analysis (PCA) and Discrete Wavelet Transform (DWT) approaches, and then feature extraction. This approach is more efficient for identifying the brain tumor MRI image data. Following the emergence of numerous research projects utilizing various data augmentation approaches [31], we may divide them into two primary categories: (1) conventional transformations based on affine image transformation and color alteration and (2) Generative Adversarial Networks (GANs), a technology that relies on the unsupervised creation of fresh images while utilizing the min-max approach. In [32], researchers proposed a machine learning model to enhance the accuracy with which diseases like heart failure (HF) may be predicted. In [33] [PERSON], [PERSON] suggested a method with four unique stages: segmentation, classification, feature extraction, and feature selection brain MRI image noise is first removed, and then features are extracted using dominant run length and co-occurrence texture features as the primary preprocessing steps. To classify MRI scan images as tumor or non-tumors, the classification stage uses a Recurrent Neural Network (RNN) model. Although the suggested method yielded a 96.26% accuracy rate, it is considered inadequate. We have provided a detailed comparison report among the different proposed methodologies in Table 1.  
The study's results highlight a common problem with current models: they are unable to classify brain tumors with greater accuracy due to a lack of generalization capacity. Therefore, there is a great problem in creating a reliable model with better classification accuracy. Inspired by this, we redirected our efforts to develop a robust and precise deep learning (DL) model to assist physicians in brain tumor diagnosis.