Pancreatic cancer remains one of the most lethal malignancies with less than 10% five-year survival rates, primarily due to late-stage diagnosis and limited early detection capabilities. Current diagnostic methods are expensive, time-intensive, and often inadequate for widespread screening applications. This study presents a novel, cost-effective approach using near-infrared (NIR) hyperspectral imaging combined with advanced machine learning for automated pancreatic tissue classification. We have developed a comprehensive pipeline incorporating autoencoder-based spatial feature extraction, multi-method consensus outlier detection, and systematically optimized neural network classifiers to distinguish between cancerous and non-cancerous pancreatic tissue samples. Our methodology was evaluated on 78 tissue microarray samples, with rigorous quality control yielding a final dataset of 69 high-quality specimens. The optimized classification model achieved 84% balanced accuracy using leave-one-out cross-validation, representing a 10% point improvement over conventional FICA+SVM approaches (74.0%) and approaching the performance of expensive conventional histopathological methods. Key technical innovations include consensus-based outlier detection, systematic hyperparameter optimization revealing optimal single-layer architectures with ELU activation, and interpretable attention mechanisms for diagnostic decision support. The demonstrated cost-effectiveness of NIR instrumentation combined with robust classification performance positions this approach as a promising pathway toward accessible, real-time pancreatic cancer screening tools that could significantly impact early detection rates and patient outcomes in diverse clinical settings.