ArticleScientific reports2025
An automated deep learning framework for brain tumor classification using MRI imagery.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Brain Tumor Segmentation and Grading on MRI Using Deep Learning: A Systematic Literature Review and Benchmark-Driven Comparative Analysis.Diagnostics (Basel, Switzerland) · 2026Review
- [Application of hybrid attention guided dual-path residual learning in magnetic resonance imaging diagnosis].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2026Article
- A Multilevel Deep Learning Model for Automated Brain Tumor Segmentation Using Magnetic Resonance Images.Diagnostics (Basel, Switzerland) · 2026Article
- Cross-attention guided explainable deep transformer model for multi-level classification of rare neurological disorders using MRI images.Scientific reports · 2026Article
- BRAIN-META: A reproducible CNN-vision transformer meta-ensemble pipeline for explainable brain tumor classification.MethodsX · 2026Article
- Multi-Chaotic HEOA for Hardware-Aware Neural Architecture Search: Brain Tumor Classification on FPGA.Sensors (Basel, Switzerland) · 2026Article
- An advanced hybrid deep learning framework for high-precision brain tumor detection and classification in MRI scans.Scientific reports · 2026Article
- Dual-path deep learning framework for accurate and interpretable brain tumor diagnosis.BMC medical informatics and decision making · 2026Article
- Voxelwise correlation of neighbors as a hypothesis driven framework for characterizing white matter lesion heterogeneity.Scientific reports · 2026Article
- Enhancing chronic disease management: hybrid graph networks and explainable AI for intelligent diagnosis.Scientific reports · 2026Article
- Bridging modalities: a deep learning framework for brain tumor classification via CT-MRI integration and model fusion.Frontiers in computational neuroscience · 2026Article
- A hybrid optimized framework with energy shape prior segmentation for brain tumor detection in MRI images.Digital healthArticle
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6 authors.
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Abstract
The precise and timely diagnosis of brain tumors is essential for accelerating patient recovery and preserving lives. Brain tumors exhibit a variety of sizes, shapes, and visual characteristics, requiring individualized treatment strategies for each patient. Radiologists require considerable proficiency to manually detect brain malignancies. However, tumor recognition remains inefficient, imprecise, and labor-intensive in manual procedures, underscoring the need for automated methods. This study introduces an effective approach for identifying brain lesions in magnetic resonance imaging (MRI) images, minimizing dependence on manual intervention. The proposed method improves image clarity by combining guided filtering techniques with anisotropic Gaussian side windows (AGSW). A morphological analysis is conducted prior to segmentation to exclude non-tumor regions from the enhanced MRI images. Deep neural networks segment the images, extracting high-quality regions of interest (ROIs) and multiscale features. Identifying salient elements is essential and is accomplished through an attention module that isolates distinctive features while eliminating irrelevant information. An ensemble model is employed to classify brain tumors into different categories. The proposed technique achieves an overall accuracy of 99.94% and 99.67% on the publicly available brain tumor datasets BraTS2020 and Figshare, respectively. Furthermore, it surpasses existing technologies in terms of automation and robustness, thereby enhancing the entire diagnostic process.
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