Leakage-Aware Evaluation of a Dual-Backbone Feature-Fusion Ensemble for Multi-Class Brain Tumor MRI Classification (Published)
Deep-learning studies of brain tumor MRI classification often report accuracies close to or above 99%, yet public benchmarks contain duplicate and near-duplicate images that can leak between training and test sets. This study audits the widely used 7,200-image Brain Tumor MRI Dataset and proposes a leakage-aware protocol that removes exact duplicates and groups near-duplicates, using perceptual hashing with pixel correlation, before a group-stratified split. The audit shows that 36.25% of the official test images have a near-duplicate in the official training set. Under the leakage-free protocol, an EfficientNetV2-B0 + ResNet50V2 feature-fusion network and a three-model ensemble were trained with test-time augmentation. The ensemble, selected on validation data alone, achieved 97.80% accuracy (95% CI 96.86–98.64%), macro-F1 of 0.978 and ROC-AUC of 0.998 on 954 unseen images, missing one of 740 tumors; the fusion model alone reached 98.32%. Rigorous de-duplication thus yields credible, still clinically promising, performance estimates.
Keywords: Brain tumor classification, Data Leakage, Ensemble learning, Feature fusion, MRI, transfer learning
Data Leakage Detection (Published)
We study the following problem: A data distributor has given sensitive data to a set of supposedly trusted agents (third parties). Some of the data are leaked and found in an unauthorized place. The distributor must assess the likelihood that the leaked data came from one or more agents, as opposed to having been independently gathered by other means. We propose data allocation strategies (across the agents) that improve the probability of identifying leakages. These methods do not rely on alterations of the released data (e.g., watermarks). In some cases, we can also inject realistic but fake data records to further improve our chances of detecting leakage and identifying the guilty party.
Keywords: Allocation Strategies, Data Leakage, Data Privacy, Fake Records, Leakage