Bald eagle-optimized transformer networks with temporal-spatial mid-level features for pancreatic tumor classification.

The classification and diagnosis of pancreatic tumors present significant challenges due to their inherent complexity and variability. Traditional methods often struggle to capture the dynamic nature of these tumors, highlighting the need for advanced techniques that improve precision and robustness. This study introduces a novel approach that combines temporal-spatial mid-level features (CTSF) with bald eagle search (BES) optimized transformer networks to enhance pancreatic tumor classification. By leveraging temporal-spatial features that encompass both spatial structure and temporal evolution, we employ the BES algorithm to optimize the vision transformer (ViT) and swin transformer (ST) models, significantly enhancing their capacity to process complex datasets. The study underscores the critical role of temporal features in pancreatic tumor classification, enabling the capture of changes over time to improve our understanding of tumor progression and treatment responses. Among the models evaluated-GRU, LSTM, and ViT-the ViTachieved superior performance, with accuracy rates of 94.44%, 89.44%, and 87.22% on the TCIA-Pancreas-CT, Decathlon Pancreas, and NIH-Pancreas-CT datasets, respectively. Spatial features extracted from ResNet50, VGG16, and ST were also essential, with the ST model attaining the highest accuracy of 95.00%, 95.56%, and 93.33% on the same datasets. The integration of temporal and spatial features within the CTSF model resulted in accuracy rates of 96.02%, 97.21%, and 95.06% for the TCIA-Pancreas-CT, Decathlon Pancreas, and NIH-Pancreas-CT datasets, respectively. Furthermore, optimization techniques, particularly hyperparameter tuning, further enhanced performance, with the BES-optimized model achieving the highest accuracy of 98.02%, 98.92%, and 98.89%. The superiority of the CTSF-BES approach was confirmed through the Friedman test and Bonferroni-Dunn test, while execution time analysis demonstrated a favourable balance between performance and efficiency.