[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-374654-105":59,"doc-detail-374654-en":131},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":124,"head_meta":126,"extra_data":128,"updated_unix":130},105,"en","hyperspectral-imaging-for-enhanced-skin-cancer-classification-using-machine-learning","Hyperspectral Imaging for Enhanced Skin Cancer Classification Using Machine Learning","","Objective: Improve early diagnosis of skin cancer by addressing the difficulty of distinguishing AK from BCC and SK, whose clinical appearances often look similar. Method: Propose Spectrum-Aided Vision Enhancer (SAVE), a hyperspectral-imaging-based system that converts RGB images into narrow-band imagery to boost contrast of cancerous regions. Results: Evaluate ten machine learning models (e.g., CNN, RF, YOLOv8, SVM variants, ResNet50, MobileNetV2) and show SAVE increases accuracy, sensitivity, and specificity over traditional RGB imaging. Conclusions: Provide clinicians an accurate, early diagnostic support tool to reduce misclassification and improve patient outcomes.",{"@graph":69,"@context":123},[70,84,106],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":35,"@type":76,"position":81},"https://docshare.wps.com/document/healthcare/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/hyperspectral-imaging-for-enhanced-skin-cancer-classification-using-machine-learning/374654/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/hyperspectral-imaging-for-enhanced-skin-cancer-classification-using-machine-learning/374654.png","ImageObject",300,407,{"name":92,"@type":93},"Đào","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-11-01","2026-09-23",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",13,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119],{"name":110,"@type":111,"acceptedAnswer":112},"What problem does the paper address in skin cancer diagnosis?","Question",{"text":113,"@type":114},"It targets the challenge of early and accurate differentiation of AK, BCC, and SK, which can appear similar on clinical images, leading to misdiagnosis and delayed care.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"How does the proposed SAVE approach work?",{"text":118,"@type":114},"SAVE uses hyperspectral imaging to enhance visualization by converting RGB images into narrow-band imagery, increasing contrast between cancerous lesions and normal tissue.",{"name":120,"@type":111,"acceptedAnswer":121},"Which machine learning models are evaluated for classification?",{"text":122,"@type":114},"The study tests ten algorithms, including convolutional neural networks, random forest, YOLOv8, support vector machine variants, and architectures such as ResNet50 and MobileNetV2.","https://schema.org",{"og:url":83,"og:type":125,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":127,"canonical":83},"index,follow",{"doc_id":129,"site_id":62},374654,1790470078,{"code":4,"msg":5,"data":132},{"doc_id":129,"user_id":133,"nickname":92,"user_avatar":134,"doc_module":4,"category_id":34,"category_name":35,"doc_title":65,"doc_description":67,"doc_content":135,"file_id":136,"file_url":137,"file_type":138,"file_size":139,"view_count":105,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":56,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":144,"read_time":145},1374402968488,"https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0","Article  \nHyperspectral Imaging for Enhanced Skin Cancer Classification Using Machine Learning  \nTeng-Li Lin 1, Arvind Mukundan 2,3, Riya Karmakar 2, Praveen Avala 4, Wen-Yen Chang 5,* and Hsiang-Chen Wang 2,6, *  \nAcademic Editors: Luca Mesin, Cosimo Ieracitano and Xuejun Zhang Received: 13 May 2025  \nRevised: 22 June 2025  \nAccepted: 9 July 2025  \nPublished: 11 July 2025  \nCitation: Lin, T.-L.; Mukundan, A.; Karmakar, R.; Avala, P.; Chang, W.-Y.; Wang, H.-C. Hyperspectral Imaging for Enhanced Skin Cancer Classification Using Machine Learning. Bioengineering 2025, 12, 755. [https://doi.org/](https://doi.org/)[ ](https://doi.org/)[10.3390/bioengineering12070755](10.3390/bioengineering12070755)  \n[Copyright:](Copyright:) © 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://creativecommons.org/](https://creativecommons.org/)[ ](https://creativecommons.org/)[licenses/by/4.0/](licenses/by/4.0/)) .  \n1 Department of Dermatology, Dalin Tzu Chi Hospital, No. 2, Min-Sheng Rd., Dalin Town, Chiayi 62247, Taiwan; [tanglilin1121@hotmail.com](tanglilin1121@hotmail.com)  \n2 Department of Mechanical Engineering, National Chung Cheng University, 168, University Rd., Min Hsiung, Chiayi 62102, Taiwan; [d09420003@ccu.edu.tw](d09420003@ccu.edu.tw) (A.M.); [karmakarriya345@gmail.com](karmakarriya345@gmail.com) (R.K.)  \n3 Department of Biomedical Imaging, Chennai Institute of Technology, Sarathy Nagar, Chennai 600069, Tamil Nadu, India  \n4 Department of Computer Science Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, No. 42, Avadi-Vel Tech Road Vel Nagar, Avadi, Chennai 600062, Tamil Nadu, India; [vtu17283@veltech.edu.in](vtu17283@veltech.edu.in)  \n5 Department of General Surgery, Kaohsiung Armed Forces General Hospital, 2, Zhongzheng 1st. Rd., Kaohsiung City 80284, Taiwan  \n6 Technology Development, Hitspectra Intelligent Technology Co., Ltd., 4F., No. 2, Fuxing 4th Rd., Qianzhen Dist., Kaohsiung City 80661, Taiwan  \n* [Correspondence: wenyen85@gmail.com](Correspondence: wenyen85@gmail.com) (W.-Y.C.); [hcwang@ccu.edu.tw](hcwang@ccu.edu.tw) (H.-C.W.)  \nAbstract  \nObjective: The classification of skin cancer is very helpful in its early diagnosis and treatment, considering the complexity involved in differentiating AK from BCC and SK. These conditions are generally not easily detectable due to their comparable clinical presentations. Method: This paper presents a new approach to hyperspectral imaging for enhancing the visualization of skin lesions called the Spectrum-Aided Vision Enhancer (SAVE), which has the ability to convert any RGB image into a narrow-band image (NBI) by combining hyperspectral imaging (HSI) to increase the contrast of the area of the cancerous lesions when compared with the normal tissue, thereby increasing the accuracy of classification. The current study investigates the use of ten different machine learning algorithms for the purpose of classification of AK, BCC, and SK, including convolutional neural network (CNN), random forest (RF), you only look once (YOLO) version 8, support vector machine (SVM), ResNet50, MobileNetV2, Logistic Regression, SVM with stochastic gradient descent (SGD) Classifier, SVM with logarithmic (LOG) Classifier and SVM-Polynomial Classifier, in assessing the capability of the system to differentiate AK from BCC and SK with heightened accuracy. Results: The results demonstrated that SAVE enhanced classification performance and increased its accuracy, sensitivity, and specificity compared to a traditional RGB imaging approach. Conclusions: This advanced method offers dermatologists a tool for early and accurate diagnosis, reducing the likelihood of misclassification and improving patient outcomes.  \nKeywords: skin cancer; hyperspectral imaging; spectrum-aided vision enhancer; convolutional neural network; yolo; ran","cbCaiv6yJHwC5Mbl","https://ap.wps.com/l/cbCaiv6yJHwC5Mbl","pdf",1476130,"English","# Abstract\n# 1. Introduction\n## Skin cancer background and classification\n## Motivation for automated image-based diagnosis\n## Related work in machine learning for skin cancer","[{\"question\":\"What problem does the paper address in skin cancer diagnosis?\",\"answer\":\"It targets the challenge of early and accurate differentiation of AK, BCC, and SK, which can appear similar on clinical images, leading to misdiagnosis and delayed care.\"},{\"question\":\"How does the proposed SAVE approach work?\",\"answer\":\"SAVE uses hyperspectral imaging to enhance visualization by converting RGB images into narrow-band imagery, increasing contrast between cancerous lesions and normal tissue.\"},{\"question\":\"Which machine learning models are evaluated for classification?\",\"answer\":\"The study tests ten algorithms, including convolutional neural networks, random forest, YOLOv8, support vector machine variants, and architectures such as ResNet50 and MobileNetV2.\"}]","Hyperspectral Imaging for Enhanced Skin Cancer Classification Using Machine Learning | PDF",1790202103,48]