biostudies-literatureFang YJNational Science and Technology Council, the Republic of China2732https://www.ebi.ac.uk/biostudies/studies/S-EPMC12607974biostudies-literatureUnknown15(21)<b>Background/Objective</b>: Esophageal cancer (EC) is a major global health issue due to its high mortality rate, as patients are often diagnosed at advanced stages. This research examines whether the Spectrum-Aided Vision Enhancer (SAVE), a hyperspectral imaging (HSI) technique, enhances endoscopic image categorization for superior diagnostic outcomes compared to traditional White Light Imaging (WLI) and Narrow Band Imaging (NBI). <b>Methods</b>: A dataset including 2400 photos categorized into eight disease types from National Taiwan University Hospital Yun-Lin Branch was utilized. Multiple machine learning and deep learning models were developed, including logistic regression, VGG16, YOLOv8, and MobileNetV2. SAVE was utilized to transform WLI photos into hyperspectral representations, Diagnostics (Basel, Switzerland)Transforming Endoscopic Image Classification with Spectrum-Aided Vision for Early and Accurate Cancer Identification.PMC12607974NSTC 113-2221-E-194-011-MY3Huang CWKarmakar RWang HCFang YJMukundan ANagisetti YLee KHfalseTransforming Endoscopic Image Classification with Spectrum-Aided Vision for Early and Accurate Cancer Identification.<b>Background/Objective</b>: Esophageal cancer (EC) is a major global health issue due to its high mortality rate, as patients are often diagnosed at advanced stages. This research examines whether the Spectrum-Aided Vision Enhancer (SAVE), a hyperspectral imaging (HSI) technique, enhances endoscopic image categorization for superior diagnostic outcomes compared to traditional White Light Imaging (WLI) and Narrow Band Imaging (NBI). <b>Methods</b>: A dataset including 2400 photos categorized into eight disease types from National Taiwan University Hospital Yun-Lin Branch was utilized. Multiple machine learning and deep learning models were developed, including logistic regression, VGG16, YOLOv8, and MobileNetV2. SAVE was utilized to transform WLI photos into hyperspectral representations, 2025-01-01T00:00:00Z2025 Oct2026-05-18T03:17:32.903Z2026-05-18T03:12:45.817ZS-EPMC126079744122602310.3390/diagnostics15212732