VGG19 in MRI Brain Tumor Classification: A Systematic Review of Validation Rigor and Clinical Readiness

Abstract

VGG19 remains a widely used reference backbone for MRI-based brain tumor classification; however, the literature is difficult to interpret because model modifications, dataset provenance, data-splitting strategies, and evaluation practices vary substantially. This systematic review provides a VGG19-centered evidence map linking architectural adaptations to validation rigor and indicators of clinical readiness. The review was updated in accordance with PRISMA 2020 principles. Searches were conducted in PubMed, IEEE Xplore, ScienceDirect, and SpringerLink, supplemented by citation searching, to identify peer-reviewed studies published from January 2015 through December 2025; the search was last updated on July 27, 2026. Because the original database export files were not retained, database-specific retrieval and duplicate counts are not reported. The final evidence base was reconciled at the reference level: 50 unique references were classified, 20 contextual or methodological sources were separated, and 30 empirical studies were included. Data were extracted on classification task, MRI source, class structure, role of VGG19, preprocessing procedures, splitting unit, validation design, performance metrics, interpretability, and reproducibility. Reporting quality was appraised using the CLAIM 2024 domains. Results were not statistically pooled because of substantial outcome heterogeneity; instead, a quantitative descriptive synthesis and structured comparative evidence map were used. Research output increased after 2021. VGG19 was employed as a stand-alone classifier, transfer-learning feature extractor, fine-tuned backbone, hybrid-model component, ensemble member, and component of explainable and federated-learning frameworks. All 30 studies reported accuracy or an accuracy-derived outcome; 25 reported class-specific metrics, 12 employed cross-validation, six included external or distributed validation, seven incorporated explainability methods, and four provided code or other implementation materials. High classification accuracies were frequently reported for Figshare and related contrast-enhanced T1-weighted MRI datasets; however, the strength of the evidence was often limited by image-level data splitting, incomplete reporting of patient provenance, lack of calibration assessment, and limited multicenter validation. The principal contribution of this review is a three-axis synthesis demonstrating that architectural improvements should be interpreted jointly with validation rigor and evidence of clinical readiness. Fine-tuned and hybrid VGG19-based systems represent competitive baselines; however, reliable clinical translation requires patient-level data separation, external validation, class-specific performance reporting, calibration assessment, explainability, and transparent documentation of computational methods and implementation.