Integrating AI with Advanced Hyperspectral Imaging for Enhanced Classification of Selected Gastrointestinal Diseases

Ulcerative colitis, polyps, esophagitis, and other gastrointestinal (GI) diseases significantly impact health, making early detection crucial for reducing mortality rates and improving patient outcomes. Traditional white light imaging (WLI) is commonly used during endoscopy to identify abnormalities in the gastrointestinal tract. However, insufficient contrast often limits its effectiveness, making it challenging to distinguish between healthy and unhealthy tissues, particularly when identifying subtle mucosal and vascular abnormalities. These limitations have prompted the need for more advanced imaging techniques that enhance pathological visualization and facilitate early diagnosis. Therefore, this study investigates the integration of the Spectrum-Aided Vision Enhancer (SAVE) mechanism to improve WLI images and increase disease detection accuracy. This approach transforms standard WLI images into hyperspectral imaging (HSI) representations, creating narrow-band imaging (NBI-like) visuals with enhanced contrast and tissue differentiation, thereby improving the visualization of vascular and mucosal structures critical for diagnosing GI disorders. This transformation allows for a clearer representation of blood vessels and membrane formations, which is essential for determining the presence of GI diseases. The dataset for this study comprises WLI images alongside SAVE-enhanced images, including four categories: ulcerative colitis, polyps, esophagitis, and healthy GI tissue. These images are organized into training, validation, and test sets to develop a deep learning-based classification model. Utilizing principal component analysis (PCA) and multiple regression analysis for spectral standardization ensures that the improved images retain spectral characteristics that are vital for clinical applications. By merging deep learning techniques with advanced imaging enhancements, this study aims to create an artificial intelligence (AI)-driven diagnostic system capable of early and accurate detection of GI diseases. InceptionV3 attained an overall accuracy of 94% in both scenarios; SAVE produced a modest enhancement in the ulcerative colitis F1-score from 92% to 93%, while the F1-scores for other classes exceeded 96%. SAVE resulted in a 10% increase in YOLOv8x accuracy, reaching 89%, with ulcerative colitis F1 improving to 82% and polyp F1 rising to 76%. VGG16 enhanced accuracy from 85% to 91%, and the F1-score for polyps improved from 68% to 81%. These findings confirm that SAVE enhancement consistently improves disease classification across diverse architectures, offers a practical, hardware-independent approach to hyperspectral-quality images, and enhances the accuracy of gastrointestinal screening. Furthermore, this research seeks to provide a practical and effective solution for clinical applications, improving diagnostic accuracy and facilitating superior patient care.