Software-Based Transformation of White Light Endoscopy Images to Hyperspectral Images for Improved Gastrointestinal...

Background/Objectives: Gastrointestinal diseases (GID), such as oesophagitis, polyps, and ulcerative colitis, contribute significantly to global morbidity and mortality. Conventional diagnostic methods employing white light imaging (WLI) in wireless capsule endoscopy (WCE) provide limited spectrum information, therefore influencing classification performance. Methods: A new technique called Spectrum Aided Vision Enhancer (SAVE), which converts traditional WLI images into hyperspectral imaging (HSI)-like representations, hence improving diagnostic accuracy. HSI involves the acquisition of image data across numerous wavelengths of light, extending beyond the visible spectrum, to deliver comprehensive information regarding the material composition and attributes of the imaged objects. This technique facilitates improved tissue characterisation, rendering it especially effective for identifying abnormalities in medical imaging. Using a carefully selected dataset consisting of 6000 annotated photos taken from the KVASIR and ETIS-Larib Polyp Database, this work classifies normal, ulcers, polyps, and oesophagitis. The performance of both the original WLI and SAVE transformed images was assessed using advanced deep learning architectures. The principal outcome was the overall classification accuracy for normal, ulcer, polyp, and oesophagitis categories, contrasting SAVE-enhanced images with standard WLI across five deep learning models. Results: The principal outcome of this study was the enhancement of diagnostic accuracy for gastrointestinal disease classification, assessed through classification accuracy, precision, recall, and F1 score. The findings illustrate the efficacy of the SAVE method in improving diagnostic performance without requiring specialised equipment. With the best accuracy of 98% attained using EfficientNetB7, compared to 97% with WLI, experimental data show that SAVE greatly increases classification metrics across all models. With relative improvement from 85% (WLI) to 92% (SAVE), VGG16 showed the highest accuracy. Conclusions: These results confirm that the SAVE algorithm significantly improves the early identification and classification of GID, therefore providing a potential development towards more accurate, non-invasive GID diagnostics with WCE.