Explainable AI for Biometric Brain Modeling: 3D Anatomical Analysis Across Gender and Aging

Abstract

This thesis investigates the use of deep learning models for predicting sex and age from structural MRI data, with a focus on interpretability. Three architectures were trained: a ResNet, a 3D CNN, and an Ensemble model. The Automated Anatomical Labeling (AAL) atlas was used to parcellate the brain into 116 regions, enabling a region based occlusion framework. Two complementary approaches were applied: inverse occlusion, where predictions rely on a single active region, and normal occlusion, where one region is masked while the remainder of the brain is preserved. All models achieved high training accuracies (95–99%), but their performance dropped notably on held out 70/15/15 splits, reflecting overfitting and emphasizing the importance of split evaluation. Inverse occlusion consistently identified plausible neuroanatomical markers, including the cerebellar Crus I, calcarine cortex, precentral and postcentral gyri, precuneus, and inferior temporal lobe. In contrast, normal occlusion produced flat or inconsistent results, suggesting reliance on global artifacts or scanner fingerprints rather than region specific features. These findings show that region only occlusion provides more reliable insights into localized brain structure differences than conventional occlusion. Key limitations include dependence on preprocessing pipelines, restricted dataset size, computational demands, and reliance on atlas based parcellation. Despite these, the framework demonstrates a reproducible method for evaluating the regional basis of deep learning predictions in neuroimaging. Future work should expand dataset coverage, refine preprocessing, and extend occlusion analysis to combinations of regions to capture network level effects. This work contributes a regional explainability framework to improve the interpretability and reliability of deep learning in brain imaging.