Explainable brain tumor detection in skull base CT using continuous neural representations: comparative evaluation of...

Reliable brain tumor detection in CT remains challenging due to low soft-tissue contrast, skull base complexity, and imaging artifacts. This study evaluates whether continuous neural representation learning improves classification performance and interpretability for CT-based brain tumor detection. We performed a comparative evaluation of coordinate-based neural models, neural operators, and transformer-based vision models using a skull base CT dataset from Gazi University Faculty of Medicine. The dataset includes 200 patients and 40,000 slices (12,000 tumor-positive, 28,000 tumor-negative). An automated multi-stage slice selection framework was developed to extract three anatomically consistent skull base slices per patient using similarity-based ranking and anatomical validation. Ten models were evaluated, including Neural Fields, Implicit Neural Representations, DeepONet, HyperNetworks, FunCNets, Rational Neural Networks, Fourier Neural Networks, and ViT-Base/Small/Large. Neural Fields achieved the best performance with 98.90% accuracy, 98.90% sensitivity, 98.90% specificity, 0.978 MCC, and 0.990 AUC. FunCNets ranked second overall while DeepONet achieved the highest sensitivity but lower overall balance. Transformer-based models underperformed compared to neural representation approaches. Statistical analysis confirmed significant performance differences with Neural Fields significantly outperforming competing methods. LIME analysis showed that Neural Fields focused on clinically relevant lesion regions while reducing influence from skull base artifacts. Continuous neural representations, particularly Neural Fields, provide superior accuracy and robustness for CT-based brain tumor classification compared to transformer and operator-based models. The findings demonstrate that neural field-based learning offers both high diagnostic performance and improved interpretability, supporting its potential for clinical decision-support in challenging skull base CT analysis.