Enhancing brain tumor detection through deep learning and explainable AI techniques.

Overview

1. Dept. of Electronics and Communications Engineering, Zagazig University, P.O. 44519, Zagazig, Egypt
2. Department of Electrical and Computer Engineering, Faculty of Engineering, King Abdulaziz University, 21589 Jeddah, Saudi Arabia
3. College of Engineering, University of Business and Technology, 23435 Jeddah, Saudi Arabia
4. Department of Engineering Mathematics and Physics, Faculty of Engineering, Zagazig University, P.O. 44519, Zagazig, Egypt

Abstract

Brain tumors are a leading cause of cancer-related mortality, and manual MRI screening remains time-consuming and observer-dependent. Deep learning (DL) offers automated detection, but clinical translation requires rigorous validation and interpretability. This study introduces a DL framework for brain tumor detection that addresses two major challenges in medical AI: limited dataset availability and lack of interpretability. Preliminary experiments identified InceptionV3 optimized with Nadam as the optimal architecture. To ensure robust validation, this model was retrained using patient-wise stratified fivefold cross-validation on 90% of the data incorporating augmentation and minority oversampling to prevent data leakage. This achieved an overall accuracy of 98.3 ± 0.9%. The final model was then trained on the entire development set using the optimal configuration, thereby leveraging all available labeled data to maximize learning capacity and enhance generalization. Performance evaluation was conducted on three levels: (i) a held out internal test set (10% of the data) for internal assessment, (ii) an external dataset of 3000 unseen images for independent validation, and (iii) quantitative explainable AI (XAI) analyses performed on both internal and external test datasets. The proposed model achieved perfect classification metrics on the internal test set, with 100% accuracy and minimal loss (0.01), and demonstrated strong generalizability on the external dataset with 96% accuracy and minimal loss (0.11). Quantitative XAI analysis demonstrated high faithfulness (Grad-CAM vs. occlusion sensitivity correlation exceeded 0.8), causal importance (top-10% occlusion drop 44% vs. 9% for random occlusion), and specificity to learned weights (Spearman correlation ≈ − 0.01). The proposed pipeline establishes a rigorous, transparent framework for data-limited medical imaging, demonstrating high diagnostic performance with clinically aligned explanations and providing a reliable foundation for trustworthy AI in brain tumor detection.

Supplementary Information: The online version contains supplementary material available at 10.1038/s41598-026-60334-y.

Reproduced under the paper's license (CC BY), from the paper cited above.

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Data

Datasets cited

• kaggle.com/datasets/ahmedhamada0, at Kaggle; found in “Data availability”
• kaggle.com/navoneel/brain-mri-images-for-brain-tumor-detection—br35h, at Kaggle; found in “Data availability”

Data availability

This study utilized two publicly available datasets for research purposes. —Brain MRI Images for Brain Tumor Detection (Chakrabarty, N., Kaggle, 2019). Available at: https://www.kaggle.com/navoneel/brain-mri-images-for-brain-tumor-detection—Br35H: Brain Tumor Detection 2020 (Hamada, A., Kaggle, 2020). Available at: [https://www.kaggle.com/datasets/ahmedhamada0/brain-tumor-detection].

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

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Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 7 authors, 12 keywords, 6 MeSH terms, 1 funder, 33 references.

Cite

This paper

Hassan, S. A., Hathah, A., Elnokity, O. E., Morfeq, A., Abdelfattah, W., Ateya, A. A., & Elsawy, N. (2026). Enhancing brain tumor detection through deep learning and explainable AI techniques. Scientific reports, 16(1), 20558. https://doi.org/10.1038/s41598-026-60334-y

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