## 2 Related Work  
Efficient detection and classification of brain tumors using magnetic resonance imaging (MRI) are essential for determining the best course of treatment. Various methodologies have been developed for using MRI to detect brain tumors. In a study by [28], a convolutional neural network (CNN) with maximum and minimum normalization was used to classify 3260 MRI images into meningioma, pituitary, glioma, and no tumor categories. The study proposed a modified version of EfficientNet, which achieved a training accuracy of 99.97% and a testing accuracy of 98.78%. The study [29] focused on classifying MRI images as normal or abnormal using the LeNet architecture. The model included age and gender factors and performed better than the CNN-DNN and SVM models, achieving 88% accuracy, an 8% improvement over CNN-DNN and a 6% improvement over SVM. It also surpassed the AlexNet model, which had a 64% accuracy rate. In [30], a hybrid model called DeepTumorNet was introduced to classify pituitary, glioma, and meningioma brain tumors. The model, which extended the GoogLeNet architecture by adding 15 layers, achieved 99.6% accuracy, 99.67% precision, 99.66% F1 score, and 100% recall. It compared several pretrained models, including ResNet50, MobileNetV2, and AlexNet. Another study [31] used multi-phase support vector machines (M-SVM) to classify pituitary adenoma, meningioma, and glioma tumors. Their model, which included a 17-layer deep neural network for tumor segmentation and feature extraction using a modified MobileNetV2, achieved 97.47% accuracy on the BraTS 2018 dataset and 98.92% on the Figshare dataset. Similarly, ref. [32] developed a hybrid model combining GoogLeNet and CNN for bilateral brain tumor classification. By modifying both networks, the model achieved 99.51% precision, 99% recall, 98.90% F1 score, and 98.50% accuracy, outperforming models like VGG-16, MobileNetV2, and ResNet. Another study [33] classified glioma tumors using support vector machines (SVMs) for classification and convolutional neural networks (CNNs) for feature extraction. The model achieved 96.19% accuracy for high-grade glioma (HGG) and 95% for low-grade glioma (LGG) based on features like necrosis and edema. In [34], CNNs with log-polar transforms (LPTs) were used to distinguish between malignant and benign tumors. The model, incorporating LPTs to handle image distortion, achieved around 96% success in classifying brain MRI images with rotation and scale invariance. Lastly, ref. [35] proposed a multi-class classification approach using MRI data to distinguish between pituitary adenoma, meningioma, glioma, and non-cancer cases. Their multi-scale CNN model outperformed ResNet and AlexNet in both accuracy and efficiency while requiring fewer computational resources, achieving an F1 score of 91% and an accuracy of 91.2%. The authors also introduced ResNet DL, a binary tumor classification model, which achieved a 99.33% accuracy. The TumorResNet model outperformed several state-of-the-art frameworks on a shared dataset. In [36], transfer learning (TL) was applied to categorize brain tumors (BT) into subtypes such as glioma, pituitary, and meningioma. By adjusting neural weights, the TL model adapted a 22-layer CNN designed for binary classification (tumor or no tumor), achieving an accuracy of 95.75%. Another example of TL implementation is [37], which used the Xception model to extract features and classify MRI images into three tumor types, with an accuracy of 99.06%. Similarly, ref. [38] used a pretrained Google Neural Network for feature extraction and employed k-nearest neighbors (KNN) and support vector machines (SVMs) for classification, achieving a 98% accuracy rate. Ref. [39] proposed a methodology combining deep learning and machine learning for classifying real MRI images from King Abdullah Founder Hospital, achieving a remarkable 99.9% accuracy. Additionally, ref. [40] used the Cancer Genome Atlas (TCGA) dataset to classify gliomas using SVM, AdaBoost, neural networks, and decision trees, achieving a 99% accuracy rate.