A Novel Explainable Deep Learning Model for Early-Stage Brain Tumor Classification: Multi-Level Feature Fusion from Merged MRI Datasets
TL;DR
A unique explainable deep learning model that integrates Tiny-ConvNeXt and DenseNet169 using multi-stage brain tumor classification is proposed, indicating that the suggested model provides a transparent and reliable framework for brain tumor detection, with potential applications in practical clinical decision support systems.
Abstract
Early and accurate detection of brain tumors remains a major challenge in medical imaging due to limited dataset size, trained deep learning models, patient variability, and the complexity of manual interpretation; traditional approaches sometimes rely on lower-level feature extraction, which may not apply well to medical images. To address these challenges, we propose a unique explainable deep learning model that integrates Tiny-ConvNeXt and DenseNet169 using multi-stage brain tumor classification. Two clinically approved, open-access Magnetic Resonance Imaging (MRI) datasets were combined to generate a bigger, more customized dataset, and substantial data augmentation techniques were used to improve model generalization. According to experimental results, our proposed model outperforms current state-of-the-art methods with a classification accuracy of 99.74%. Additionally, statistical significance tests confirmed the incremental contribution of each model component, and ablation studies showed the robustness of the conclusions. Additionally, explainable artificial intelligence techniques like Local Interpretable Model-agnostic Explanations and Gradient-weighted Class Activation Mapping++ were employed to enhance interpretability, enabling visual explanations of tumor localization. These results indicate that the suggested model provides a transparent and reliable framework for brain tumor detection, with potential applications in practical clinical decision support systems. Received: 18 May 2025 | Revised: 29 April 2026 | Accepted: 7 July 2026 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement The data that support the findings of this study are openly available in the American Society of Clinical Oncology at https://www.cancer.org/cancer/types/brain-spinal-cord-tumors-adults/key-statistics.html, in Kaggle at https://www.kaggle.com/datasets/ahmedhamada0/brain-tumor-detection, and in Kaggle at https://www.kaggle.com/datasets/navoneel/brain-mri-images-for-brain-tumor-detection. Author Contribution Statement Md Sadi Al Huda: Conceptualization, Methodology, Validation, Formal analysis, Investigation, Writing - original draft, Writing - review & editing, Visualization, Supervision, Project administration. Kazi Tanvir: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Data curation, Visualization. Zubaida Akhter: Software, Validation, Formal analysis, Resources, Data curation, Writing - original draft, Writing - review & editing, Visualization. Md. Shahidul Khan Pappo: Validation, Writing - review & editing, Visualization. Md. Asraf Ali: Resources, Writing - review & editing, Supervision, Project administration. Nasim Ahmed: Resources, Writing - review & editing, Supervision, Project administration.