## 1 Introduction  
Brain tumors are among the most dangerous types of cancer and require precise and timely classification to improve patient outcomes. Traditional methods of brain tumor diagnosis rely heavily on manual interpretation by radiologists, which can be time-consuming, subjective, and prone to errors. Early diagnosis is crucial in determining the appropriate treatment path, including surgery, radiotherapy, or chemotherapy [1]. In recent years, integrating AI-based technologies--such as artificial neural networks (ANNs), fuzzy logic-based neural networks, and genetic algorithms--into brain tumor diagnosis has transformed the field. These AI-based technologies enable automatic, high-speed, and accurate tumor classification, enhance clinical decision-making, and reduce human error. Artificial neural networks (ANNs) have received significant attention in medical image analysis and classification due to their ability to solve complex computational problems, particularly in medical diagnostics that require high accuracy [2]. One of the key applications of ANNs is brain tumor classification (BTC). BTC systems rely on medical imaging data, such as MRI scans, to detect, diagnose, and plan treatment for brain tumors at an early stage [3]. Additionally, BTC systems assist radiologists and clinicians in accurately distinguishing between different types of brain tumors, such as gliomas, meningiomas, and pituitary tumors [1]. The typical workflow of BTC algorithms consists of several key stages: (1) data acquisition, where MRI scans are collected; (2) preprocessing, which includes noise reduction and image normalization to enhance quality; (3) segmentation, which identifies regions of interest, particularly tumor areas; (4) feature extraction, where characteristics such as tumor size and texture are identified; (5) classification, using techniques like ANNs, fuzzy logic-based neural networks (FLNNs), or genetic algorithms (GAs) to determine the tumor type; and (6) evaluation, where the model's performance is assessed based on accuracy, precision, recall, and other relevant metrics [4]. Deep learning models, a subset of ANNs, have demonstrated their ability to learn complex patterns in data. Numerous studies have explored the effectiveness of popular deep learning models, such as convolutional neural networks (CNNs), in BTC [5]. CNNs are particularly well-suited for image processing tasks and have been widely applied in medical imaging. However, despite their success, several challenges remain. One major challenge is the variability in datasets used across different studies, often leading to inconsistent performance when applied to new or diverse patient populations. Another challenge is comparing results across studies due to the lack of standardized evaluation metrics and transparent training and testing mechanisms [6]. Fuzzy logic-based neural networks (FLNNs) represent another promising BTC approach. FLNNs combine the strengths of fuzzy logic and neural networks to address uncertainties and imprecise data in medical image classification [7]. Fuzzy logic enables the system to interpret data more flexibly using linguistic variables such as \"small\", \"medium\", or \"large\" [8]. FLNNs enhance the accuracy of BTC systems by mitigating noise and ambiguity often present in medical images. Combining fuzzy rules derived from expert knowledge and neural network learning capabilities improves the system's interpretability and robustness. Another approach involves using GAs inspired by natural selection. GAs are employed to optimize the parameters of ANNs and to select the most relevant features from MRI images for accurate classification. Integrating GAs into ANNs enhances BTC accuracy by optimizing feature selection and adjusting the hyperparameters of ANN models. Moreover, the ability of GAs to evolve through crossover and mutation processes enables efficient optimization of complex models such as ANNs [9].