Abstract
This study characterizes spatial, spectral, relational, and contextual representations for binary gastric histopathological image classification using FLIGENT-Net. The framework combines an ImageNet-pretrained convolutional backbone, patch-level fast Fourier transform (FFT) descriptors, graph convolution over a cosine-similarity graph, and a Transformer Encoder. Experiments use the 160-by-160 GasHisSDB subset, a persisted stratified 60%/20%/20% partition, and one training run per configuration with seed 42. In the nine completed configurations of the revised evaluation, FLIGENT-Net achieved 97.31% accuracy, 96.57% precision, 96.61% recall, 96.59% F1-score for the Abnormal class, and 0.9964 AUC-ROC. An additional native-grid FFT control yielded 97.43% accuracy and 96.74% F1 under an alternative spectral protocol that preserved the 160-by-160 sampling grid while changing the local FFT window sizes and spectral discretization. The conventional CNN baseline (ResNet18) obtained the highest accuracy and F1-score among these completed runs, at 98.51% and 98.13%, respectively. These results do not establish a predictive advantage for the complete multi-domain architecture; rather, they characterize how the added representation domains affect predictive behavior, graph topology, computational cost, and attribution response under a common protocol. The study contributes a reproducible integration of the four representation domains and a controlled empirical characterization of their combinations. Transformer attention visualizations describe information allocation among contextualized nodes, while a common input-gradient deletion protocol examines attribution behavior. Together, these analyses identify the behavior and limitations of the implemented fusion strategy and provide a basis for its further development. Performance estimates are conditional on the available patch-level split and should not be interpreted as patient-, slide-, or source-image-independent generalization; run-to-run variability is also not estimated because one fixed seed is used per configuration.