This study explores existing research on neural networks, fuzzy logic-based models, and genetic algorithms applied to brain tumor classification. A systematic review of 53 studies was conducted following PRISMA guidelines, covering search strategy, selection criteria, quality assessment, and data extraction. Articles were collected from three scientific databases: Web of Science, Scopus, and IEEE. The review primarily focuses on practical contributions, with most studies emphasizing applications over conceptual insights. Key methods in the field demonstrate significant impact and innovation. Commonly used training and testing mechanisms include dataset splitting, augmentation, and validation techniques, highlighting their widespread adoption for performance evaluation. The analysis of evaluation metrics shows that accuracy and the DICE score are the most frequently used, alongside sensitivity, specificity, recall, and other domain-specific measures. The variety of metrics underscores the need for tailored approaches based on dataset characteristics and research objectives. By highlighting trends, challenges, and research gaps, this review provides actionable insights for advancing BTC research. It offers a comprehensive overview of techniques and evaluation methods to guide future developments in this critical domain.

Deep learning-based object detectors, particularly You Only Look Once (YOLO) architectures, have demonstrated strong performance in automated brain tumor detection. However, the impact of resolution scaling on tumor localization accuracy remains underexplored, especially under conditions where image resolution is reduced. This study aims to investigate how lowering the input resolution from 640 × 640 to 480 × 480 affects detection performance and whether optimized depth/width scaling and hyperparameter tuning can compensate for the expected loss of spatial detail. In this work, we propose an optimized YOLO-based framework for brain tumor detection and localization in MRI scans, building upon the method “Addressing the Impact of Resolution Scaling on YOLO Performance for Brain Tumor Detection through Optimized Network Depth/Width Adjustments.” Our model, an enhanced variant of the BGF-YOLO architecture, is specifically tailored for the challenges of medical imaging. The proposed network features both architectural and training-level optimizations. We used a publicly available dataset from Kaggle that consists of 500 training images, 201 validation images, and 100 test images. Experimental analysis demonstrates that while reducing input resolution alone degrades performance, integrating targeted modifications specifically increases network depth and width. In addition, advanced training strategies such as MixUp augmentation, dropout regularization, AdamW optimization, cosine learning rate scheduling, and finely tuned learning rate ranges lead to substantial performance gains. The optimized model achieves a precision of up to 0.858, a recall of 0.943, mAP50 of 0.946, and mAP50–95 of 0.672. These results not only outperform the reduced-resolution baseline but also approach, and in some cases surpass, the original high-resolution BGF-YOLO setup.