## 2 Literature Review  
Timely identification and classification of brain tumors are crucial for a patient's treatment. The implementation of automated systems has enabled healthcare professionals to enhance patient care because of remarkable technological advancements. In artificial intelligence, deep learning has gained a lot of momentum and has made major advancements in a variety of fields, including natural language processing (NLP), autonomous cars, medicine, agriculture, and large language models (LLM) [17]. The arrival of new artificial intelligence and deep learning technologies has significantly influenced the field of medical image processing, particularly in the diagnosis of diseases. Recent studies have introduced several techniques employing machine learning (ML) and deep learning (DL) algorithms for the classification of brain tumors. This section reviews research on methodologies for classifying brain tumors using ML and DL-based algorithms [18]. Deep learning is one of the most effective techniques in artificial intelligence for instructing models to generate valuable decision-making skills from data [19]. The author discussed the steps involved in creating a deep learning project for radiology before providing an outline of some potential clinical use cases that would make use of these techniques [20]. The main objective was to investigate the opportunities and difficulties of future deep learning usage in radiology practices. To classify the brain tumor, various deep learning (DL) approaches have been proposed to achieve the goal of brain tumor classification such as convolutional neural networks (CNN) [21] and long short-term memory (LSTM) [22]. In the study authors [23] proposed a CNN-based learning method to identify brain tumors on MRI pictures. This model dataset contained 3263 pictures including 500 images without tumors, 937 images of meningiomas, 901 images of pituitary tumors, and 926 images of glioma tumors. The suggested model is the best at identifying different types of brain tumors and achieved 95.99% accuracy. However, it is recommended to modify the hyperparameters of the proposed model to enhance its accuracy. A new CNN model called \"BrainMRNet\" was suggested by [PERSON] et al. [24]. The framework was created using a hyper-column method and attention segmentation. In the beginning, pre-processing was performed using BrainMRNet, an attention section that afterward carried out the approach of image augmentation, which allowed the retrieval of critical visual features subsequently transmitted to the convolutional network's layers. The dataset used for the research was composed of MRI images that are widely accessible. These images served as two labels. One showed an abnormal view, whereas the other showed a tumor. JPEG pictures were created from the original photographs. The proposed model compares with CNN models like AlexNet, VGG-16, and GoogleNet. Tumor identification using the BrainMRNet model was more accurate, with a 96.05% performed accuracy. However, the proposed model is not suitable for the multi-classification of brain tumors and can be improved and diversified. The study [25] employed CNN, DNN, and KNN to diagnose brain tumors accurately and efficiently. The segmentation of the BRATS 2019 dataset served to exhibit the relative efficacy of several deep-learning algorithms. To choose the best features, the term \"hybrid manta ray foraging optimization\" was proposed. Tumor identification was performed using CNN. The three types of tumors in the dataset were benign tumors, malignant tumors, and typical images. A comparison of various machine learning models was undertaken, and the results were helpful in more effectively and accurately identifying brain tumors. In [26], [PERSON] et al. suggested developing a novel brain tumor detection and categorization system employing well-recognized deep transfer learning models, such as Dense-Net121, VGG16, MobileNet v2, and Xception. On a benchmark dataset, the suggested strategy's effectiveness is assessed in terms of accuracy and loss.