Consistency and stability benchmarking of Grad-CAM, SHAP, and LIME for diffuse and focal brain MRI classification
Bibliographic record
Abstract
Clinical adoption of deep convolutional neural networks for brain-MRI interpretation hinges on reliable visual explanations, yet systematic head-to-head evaluations that quantify explanation robustness and reproducibility across multiple neuropathologies within a fully standardized pipeline remain scarce. This study performs a controlled comparison of Gradient-weighted Class Activation Mapping (Grad-CAM), Deep-SHAP and LIME using a single 3-D ResNet-50 backbone fine-tuned on two public datasets: Augmented Alzheimer MRI v2 (6 400 T1-weighted slices, four dementia stages) and Brain-Tumor MRI (3 264 slices, four tumor classes). Uniform preprocessing—HD-BET skull stripping, MNI-152 registration, 1 mm3 resampling and z-score normalization—minimizes scanner bias, while patient-stratified 70 : 15 : 15 splits prevent information leakage. Diagnostic performance surpasses 98 % accuracy and 0.99 AUC on both tasks, providing a robust foundation for explanation assessment. These measures aim to capture how reproducible and robust the explanations are under realistic perturbations. Grad-CAM exhibits the highest consistency (0.764 for Alzheimer’s, >0.90 for tumors) and stability (0.684 and >0.90), accurately highlighting hippocampal atrophy and tumor cores. Deep-SHAP delivers anatomically detailed but computationally intensive attributions, whereas LIME shows pronounced variability due to super-voxel segmentation. Findings recommend gradient-based explanations for rapid, focal-lesion screening and reserve SHAP for diffuse pathology requiring voxel-level justification, while cautioning that LIME demands careful parameter tuning before clinical deployment. An open-source evaluation script accompanies the study to facilitate reproducible, pathology-aware XAI benchmarking in neuro-radiology. To assist related work, the code is available at: https://github.com/fyyyaug/medical-image.git.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.037 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".