A Systematic Review Of State-Of-Art Methods Of Convolutional Neural Network Based MRI Brain Tumor Classification (2015-2025): Optimization, Augmentation, Performance Analysis, Datasets, Research Challenges, And Future Scope
DOI:
https://doi.org/10.29284/sykr7y61Keywords:
Convolutional Neural Network, MRI, Hyperparameters, Brain tumor, Detection, Classification.Abstract
Brain tumor detection and classification using magnetic resonance imaging (MRI) are essential components of modern diagnostic workflows, with convolutional neural networks (CNNs) demonstrating strong effectiveness due to their superior feature extraction and classification capabilities. However, existing research remains fragmented, showing inconsistencies in datasets, augmentation strategies, hyperparameter tuning methods, and evaluation metrics. This systematic literature review (SLR) synthesizes evidence from 132 peer-reviewed studies published between January 2015 and June 2025, following the PRISMA framework and categorizing the selected works into custom CNN architectures (n = 60) and transfer learning approaches using pretrained models (n = 72). The review addresses five research questions focusing on frequently used MRI datasets for binary and multi-class classification, commonly adopted augmentation methods, hyperparameter optimization strategies including manual, heuristic, and advanced approaches, the most widely used pretrained CNN models, and the performance metrics employed for evaluating classification effectiveness. The findings highlight key limitations such as reliance on non-clinical homogeneous datasets, underrepresentation of rare tumor subtypes, limited exploration of advanced augmentation methods, inadequate documentation of hyperparameter search spaces, and inconsistent evaluation practices. To advance the field, future research should prioritize clinically diverse datasets, adopt improved augmentation techniques such as GAN-based synthesis and domain adaptation, and incorporate systematic hyperparameter optimization strategies, including Bayesian optimization with well-defined search spaces. This SLR provides a unified synthesis of current knowledge, identifies methodological gaps, and offers clear directions for developing robust, generalizable, and clinically reliable CNN-based diagnostic models.
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