Brain Tumor Detection Technologies and Explainable Artificial Intelligence: A Review

Abstract

Brain tumor detection is a crucial task in medical diagnosis, as early detection significantly improves survival rates. Recent advancements in Artificial Intelligence (AI), particularly deep learning, have enabled automated and accurate tumor detection using medical imaging techniques such as MRI. However, most deep learning models lack interpretability, limiting their adoption in clinical practice. Explainable Artificial Intelligence (XAI) addresses this issue by providing transparency and understanding of model decisions. This paper reviews various brain tumor detection technologies, including traditional machine learning, deep learning approaches, and XAI techniques. It also discusses challenges, recent trends, and future research directions.

Introduction

The text discusses brain tumor detection using AI, particularly deep learning and Explainable AI (XAI), highlighting advancements, methodologies, and challenges.

1. Brain Tumors and Diagnosis

• Brain tumors are abnormal cell growths that may be benign or malignant.
• Early and accurate diagnosis is critical.
• Traditional biopsy is invasive; imaging-based techniques (MRI, CT, PET) provide non-invasive alternatives.
• MRI is the preferred modality for AI-based detection due to high-resolution imaging.

2. AI and Deep Learning in Tumor Detection

• Convolutional Neural Networks (CNNs) are widely used for automatic feature extraction and tumor classification.
• Popular CNN architectures: VGG16, ResNet, DenseNet, EfficientNet, MobileNet.
• Deep learning models achieve high accuracy (~98–99%).
• Advanced approaches include transfer learning, ensemble learning, and multimodal learning for better generalization.
• Traditional ML methods (SVM, KNN, Random Forest) are less accurate due to manual feature extraction.

3. Explainable Artificial Intelligence (XAI)

• CNNs are often “black boxes,” limiting clinical trust.
• XAI methods such as Grad-CAM, LIME, SHAP, Integrated Gradients help interpret model decisions.
• Benefits of XAI in tumor detection:
  • Provides visual explanations (heatmaps) of influential regions.
  • Improves clinician trust and adoption.
  • Enhances diagnostic reliability.
• Challenges include data imbalance, high computational cost, black-box complexity, and difficulty in clinical validation.

Conclusion

Brain tumor detection has significantly improved with deep learning technologies. However, the lack of interpretability remains a major concern. Explainable AI bridges this gap by making model decisions transparent and trustworthy. Future research should focus on developing accurate, efficient, and interpretable systems for real-world healthcare applications. CNN models achieve accuracy between 95%–99% XAI-integrated systems improve interpretability. Transfer learning enhances performance on small datasets. Advantages are High accuracy, Automated feature extraction, Non-invasive diagnosis, Faster processing, Improved transparency with XAI.

References

[1] Srinivas VR and Parvathi R (2026) Explainable AI-driven MRI-based brain tumor classification: a novel deep learning approach. Front. Artif. Intell. 8:1700214. doi: 10.3389/frai.2025.1700214 [2] Iftikhar S, Anjum N, Siddiqui AB, Ur Rehman M, Ramzan N. Explainable CNN for brain tumor detection and classification through XAI based key features identification. Brain Inform. 2025 Apr 30;12(1):10. doi: 10.1186/s40708-025-00257-y. PMID: 40304860; PMCID: PMC12044100. [3] K. M. Hosny et al., “Explainable AI and vision transformers for brain tumor detection,” Artificial Intelligence Review, 2025. [4] T. A. Fahim et al., “Brain tumor detection using deep neural networks: A review,” Computers in Biology and Medicine, 2025. [5] Fatma M. Talaat. ; Mohamed Shehata “Recent Advances in Signal Processing and Computer Vision” Computer Modeling in Engineering & Sciences 2025, 144(2), 2325-2358. https://doi.org/10.32604/cmes.2025.067195 Received 27 April 2025; Accepted 08 July 2025; Issue published 31 August 2025 [6] E. Gundogan et al., “Hybrid deep learning model enhanced with XAI for brain tumor classification,” Applied Sciences, 2025. [7] K. M. A. Adnan et al., “Interpretable deep learning model for brain tumor prediction,” Scientific Reports, 2025. [8] Abraham, L.A., Palanisamy, G., Veerapu, G. et al. Exploring the potential of explainable AI in brain tumor detection and classification: a systematic review. Artif Intell Rev 59, 14 (2026). https://doi.org/10.1007/s10462-025-11410-8A. [9] Akgündo?du et al., “Explainable deep learning framework for brain tumor detection,” Information Processing & Management, 2025. [10] Arth Agrawal, Jyotismita Chaki, CerebralNet meets Explainable AI: Brain tumor detection and classification with probabilistic augmentation and a deep learning approach, Biomedical Signal Processing and Control, Volume 110, Part B, 2025,108210,ISSN 1746-8094, https://doi.org/10.1016/j.bspc.2025.108210. [11] K. M. Hosny et al., “Explainable ensemble deep learning model for brain tumor detection,” Neural Computing & Applications, 2025. [12] S. Aksoy et al., “Web-deployed explainable AI system for brain tumor detection,” MDPI, 2025. [13] D. Vamsidhar et al., “Hybrid CNN and Vision Transformer for brain tumor detection,” Scientific Reports, 2025. [14] S. M. Ganie et al., “Hybrid multiscale explainable deep learning model for brain tumor diagnosis,” 2026. [15] M, M.M., T. R, M., V, V.K. et al. Enhancing brain tumor detection in MRI images through explainable AI using Grad-CAM with Resnet 50. BMC Med Imaging 24, 107 (2024). https://doi.org/10.1186/s12880-024-01292-7

Copyright

Copyright © 2026 Priyanka Kalyanrao Tawale , Amol Dadarao Wakhare, Vaibhav Tawale . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.