{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Fang YJ"],"funding":["National Science and Technology Council, the Republic of China"],"pagination":["2732"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC12607974"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["15(21)"],"pubmed_abstract":["<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, "],"journal":["Diagnostics (Basel, Switzerland)"],"pubmed_title":["Transforming Endoscopic Image Classification with Spectrum-Aided Vision for Early and Accurate Cancer Identification."],"pmcid":["PMC12607974"],"funding_grant_id":["NSTC 113-2221-E-194-011-MY3"],"pubmed_authors":["Huang CW","Karmakar R","Wang HC","Fang YJ","Mukundan A","Nagisetti Y","Lee KH"],"additional_accession":[]},"is_claimable":false,"name":"Transforming Endoscopic Image Classification with Spectrum-Aided Vision for Early and Accurate Cancer Identification.","description":"<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, ","dates":{"release":"2025-01-01T00:00:00Z","publication":"2025 Oct","modification":"2026-05-18T03:17:32.903Z","creation":"2026-05-18T03:12:45.817Z"},"accession":"S-EPMC12607974","cross_references":{"pubmed":["41226023"],"doi":["10.3390/diagnostics15212732"]}}