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Related Experiment Videos

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

Shaymaa A Hassan1, Anfal Hathah2, Omar E Elnokity2

  • 1Dept. of Electronics and Communications Engineering, Zagazig University, P.O. 44519, Zagazig, Egypt. shafayad@zu.edu.eg.

Scientific Reports
|July 4, 2026
PubMed
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This summary is machine-generated.

This study developed a deep learning framework for brain tumor detection, achieving 98.3% accuracy. The AI model provides reliable, explainable results for medical imaging, enhancing trustworthy artificial intelligence in oncology.

Area of Science:

  • Medical Imaging and Artificial Intelligence
  • Oncology and Neuroscience
  • Computer Science and Machine Learning

Background:

  • Brain tumors are a significant cause of cancer mortality, with manual MRI analysis being time-consuming and subjective.
  • Deep learning (DL) offers automated brain tumor detection, but clinical adoption is hindered by limited data and lack of interpretability.
  • Developing robust and transparent AI tools is crucial for advancing medical diagnostics.

Purpose of the Study:

  • To introduce a deep learning (DL) framework for automated brain tumor detection.
  • To address challenges of limited dataset availability and lack of interpretability in medical AI.
  • To validate the framework's performance and clinical relevance through rigorous testing and explainable AI (XAI) analysis.

Main Methods:

Keywords:
Brain tumorDeep learningExplainable AI (XAI)External validationMRIPatient-wise cross-validation

Related Experiment Videos

  • Utilized InceptionV3 optimized with Nadam architecture for brain tumor detection.
  • Employed patient-wise stratified fivefold cross-validation with data augmentation and oversampling on 90% of data.
  • Validated the model on internal (10%) and external (3000 images) test sets, incorporating quantitative explainable AI (XAI) analyses.

Main Results:

  • Achieved 98.3% accuracy during cross-validation and perfect metrics (100% accuracy) on the internal test set.
  • Demonstrated strong generalizability on an external dataset with 96% accuracy.
  • Quantitative XAI confirmed high faithfulness, causal importance, and specificity, supporting model transparency.

Conclusions:

  • The proposed DL framework offers a rigorous and transparent solution for data-limited medical imaging, particularly for brain tumor detection.
  • The model achieves high diagnostic performance with clinically aligned explanations, establishing a foundation for trustworthy AI.
  • This approach enhances the reliability and interpretability of AI in medical diagnostics, paving the way for clinical translation.