[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-seo-378054-105":3,"detail-sidebar-cat-0-en-105":81,"doc-detail-378054-en":130},{"code":4,"msg":5,"data":6},0,"ok",{"site_id":7,"language":8,"slug":9,"title":10,"keywords":11,"description":12,"schema_data":13,"social_meta":74,"head_meta":76,"extra_data":78,"updated_unix":80},105,"en","oralnet-fused-optimal-deep-features-framework-for-oral-squamous-cell-carcinoma-detection","OralNet: Fused Optimal Deep Features Framework for Oral Squamous Cell Carcinoma Detection","","Humankind is witnessing a gradual rise in cancer incidence, making early diagnosis, treatment, and follow-up protocols essential. Oral cancer, a head-and-neck malignancy, requires effective screening for timely detection. This study introduces OralNet, a histopathology-image framework for oral cancer detection, organized into four stages: data collection and preprocessing, hybrid deep and handcrafted feature extraction, feature reduction via an artificial hummingbird algorithm with serial concatenation, and binary classification with three-fold cross-validation. Evaluated on whole-slide biopsy images at 100× and 400× magnifications, OralNet used 3000 cropped and resized samples (1500 healthy, 1500 OSCC), achieving accuracy above 99.5%.",{"@graph":14,"@context":73},[15,34,56],{"@type":16,"itemListElement":17},"BreadcrumbList",[18,23,27,31],{"item":19,"name":20,"@type":21,"position":22},"https://docshare.wps.com","Home","ListItem",1,{"item":24,"name":25,"@type":21,"position":26},"https://docshare.wps.com/document/","Document",2,{"item":28,"name":29,"@type":21,"position":30},"https://docshare.wps.com/document/healthcare/","Healthcare",3,{"item":32,"name":10,"@type":21,"position":33},"https://docshare.wps.com/document/oralnet-fused-optimal-deep-features-framework-for-oral-squamous-cell-carcinoma-detection/378054/",4,{"url":32,"name":10,"@type":35,"image":36,"author":41,"headline":10,"publisher":44,"fileFormat":47,"inLanguage":8,"description":12,"dateModified":48,"datePublished":49,"encodingFormat":47,"isAccessibleForFree":50,"interactionStatistic":51},"DigitalDocument",{"url":37,"@type":38,"width":39,"height":40},"https://docshare.wps.com/thumbnails/oralnet-fused-optimal-deep-features-framework-for-oral-squamous-cell-carcinoma-detection/378054.png","ImageObject",300,407,{"name":42,"@type":43},"Alex Sinclair","Person",{"url":19,"name":45,"@type":46},"DocShare","Organization","application/pdf","2026-10-30","2026-09-24",true,{"@type":52,"interactionType":53,"userInteractionCount":55},"InteractionCounter",{"@type":54},"ViewAction",9,{"@type":57,"mainEntity":58},"FAQPage",[59,65,69],{"name":60,"@type":61,"acceptedAnswer":62},"What problem does OralNet address in oral cancer detection?","Question",{"text":63,"@type":64},"OralNet targets timely detection of oral squamous cell carcinoma using histopathology images, supporting early diagnosis and clinical follow-up decisions.","Answer",{"name":66,"@type":61,"acceptedAnswer":67},"How is OralNet structured in terms of processing stages?",{"text":68,"@type":64},"OralNet follows four stages: image collection and preprocessing, deep-and-handcrafted feature extraction, feature reduction with AHA plus serial concatenation, and binary classification with three-fold cross-validation.",{"name":70,"@type":61,"acceptedAnswer":71},"What experimental setup and performance does the study report?",{"text":72,"@type":64},"The framework was tested on whole slide biopsy images at 100× and 400×, using 3000 cropped and resized images (1500 healthy, 1500 OSCC). Results show oral cancer detection accuracy exceeding 99.5%.","https://schema.org",{"og:url":32,"og:type":75,"og:title":10,"og:site_name":45,"og:description":12},"article",{"robots":77,"canonical":32},"index,follow",{"doc_id":79,"site_id":7},378054,1791048004,{"code":4,"msg":82,"data":83},"success",[84,88,92,96,101,106,110,115,119,122,126],{"id":22,"doc_module":4,"doc_module_name":25,"category_name":85,"show_sort_weight":86,"slug":87},"Story & Novel",90,"story-novel",{"id":26,"doc_module":4,"doc_module_name":25,"category_name":89,"show_sort_weight":90,"slug":91},"Literature",80,"literature",{"id":33,"doc_module":4,"doc_module_name":25,"category_name":93,"show_sort_weight":94,"slug":95},"Exam",70,"exam",{"id":97,"doc_module":4,"doc_module_name":25,"category_name":98,"show_sort_weight":99,"slug":100},5,"Comic",60,"comic",{"id":102,"doc_module":4,"doc_module_name":25,"category_name":103,"show_sort_weight":104,"slug":105},6,"Technology",50,"technology",{"id":107,"doc_module":4,"doc_module_name":25,"category_name":29,"show_sort_weight":108,"slug":109},7,40,"healthcare",{"id":111,"doc_module":4,"doc_module_name":25,"category_name":112,"show_sort_weight":113,"slug":114},8,"Research & Report",30,"research-report",{"id":55,"doc_module":4,"doc_module_name":25,"category_name":116,"show_sort_weight":117,"slug":118},"Religion & Spirituality",20,"religion-spirituality",{"id":117,"doc_module":4,"doc_module_name":25,"category_name":120,"show_sort_weight":117,"slug":121},"World Cup","world-cup",{"id":123,"doc_module":4,"doc_module_name":25,"category_name":124,"show_sort_weight":123,"slug":125},10,"Lifestyle","lifestyle",{"id":127,"doc_module":4,"doc_module_name":25,"category_name":128,"show_sort_weight":97,"slug":129},19,"General","general",{"code":4,"msg":82,"data":131},{"doc_id":79,"user_id":132,"nickname":42,"user_avatar":133,"doc_module":4,"category_id":107,"category_name":29,"doc_title":10,"doc_description":12,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":55,"is_deleted":4,"is_public":22,"is_downloadable":22,"audit_status":22,"page_count":139,"language":140,"language_code":8,"site_id":7,"html_lang":8,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":12,"update_tm":144,"read_time":99},1099523882182,"https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8","biomolecules  \nArticle  \nOralNet: Fused Optimal Deep Features Framework for Oral Squamous Cell Carcinoma Detection  \nRamya Mohan 1, Arunmozhi Rama 1,*, Ramalingam Karthik Raja 1, Mohammed Rafi Shaik 2, *, Mujeeb Khan 2, Baji Shaik 3 and Venkatesan Rajinikanth 1  \nCitation: Mohan, R.; Rama, A.; Raja, R.K.; Shaik, M.R.; Khan, M.; Shaik, B.; Rajinikanth, V. OralNet: Fused Optimal Deep Features Framework for Oral Squamous Cell Carcinoma Detection. Biomolecules 2023, 13, 1090 . [https://doi.org/10.3390/](https://doi.org/10.3390/)[ ](https://doi.org/10.3390/)biom13071090  \nAcademic Editors: Mohsin Saleet Jafri and Vladimir N. Uversky  \nReceived: 26 April 2023  \nRevised: 17 June 2023  \nAccepted: 5 July 2023  \nPublished: 7 July 2023  \nCopyright: © 2023 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://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) .  \n1 Department of Computer Science and Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai 602105, India; [ramyanallu@saveetha.com](ramyanallu@saveetha.com) (R.M.); [sriramkarthik83@gmail.com](sriramkarthik83@gmail.com) (R.K.R.); [v.rajinikanth@ieee.org](v.rajinikanth@ieee.org) (V.R.)  \n2 Department of Chemistry, College of Science, King Saud University,  \nP.O. Box 2455, Riyadh 11451, Saudi Arabia; [kmujeeb@ksu.edu.sa](kmujeeb@ksu.edu.sa)  \n3 School of Chemical Engineering, Yeungnam University, Gyeongsan 38541, Republic of Korea; [shaikbaji@yu.ac.kr](shaikbaji@yu.ac.kr)  \n* Correspondence: [ramaa.sse@saveetha.com](ramaa.sse@saveetha.com) (A.R.); [mrshaik@ksu.edu.sa](mrshaik@ksu.edu.sa) (M.R.S.)  \nAbstract: Humankind is witnessing a gradual increase in cancer incidence, emphasizing the importance of early diagnosis and treatment, and follow-up clinical protocols. Oral or mouth cancer, categorized under head and neck cancers, requires effective screening for timely detection. This study proposes a framework, OralNet, for oral cancer detection using histopathology images. The research encompasses four stages: (i) Image collection and preprocessing, gathering and preparing histopathology images for analysis; (ii) feature extraction using deep and handcrafted scheme, extracting relevant features from images using deep learning techniques and traditional methods;(iii) feature reduction artificial hummingbird algorithm (AHA) and concatenation: Reducing feature dimensionality using AHA and concatenating them serially and (iv) binary classification and performance validation with three-fold cross-validation: Classifying images as healthy or oral squamous cell carcinoma and evaluating the framework's performance using three-fold cross-validation. The current study examined whole slide biopsy images at 100 􀀂 and 400 􀀂 magnifications. To establish OralNet's validity, 3000 cropped and resized images were reviewed, comprising 1500 healthy and 1500 oral squamous cell carcinoma images. Experimental results using OralNet achieved an oral cancer detection accuracy exceeding 99.5% . These findings confirm the clinical significance of the proposed technique in detecting oral cancer presence in histology slides.  \nKeywords: oral cancer; OSCC; VGG16; DenseNet201; OralNet; classification  \n1. Introduction  \nThe incidence of cancer in the human population is steadily increasing due to various factors, necessitating the need for appropriate screening and diagnosis to enable timely detection and treatment. Recent literature has confirmed that cancer rates are rising among individuals regardless of age, race and sex, leading to the development and implementation of numerous awareness programs and clinical protocols aimed at reducing the impact of the disease [1–3] .  \nAccording to the 2020 report by the World Health Organization (WHO)","cbCaikdAM6g6RpXM","https://ap.wps.com/l/cbCaikdAM6g6RpXM","pdf",5530891,24,"English","# Introduction\n## Background and cancer screening need\n## Oral cancer burden and risk factors\n## Clinical diagnosis and OSCC importance\n## Existing ML/DL approaches\n# Materials and Methods\n## Image collection and preprocessing\n## Feature extraction (deep and handcrafted)\n## Feature reduction and concatenation (AHA)\n## Binary classification with three-fold cross-validation\n# Results and Discussion\n## Dataset and evaluation at different magnifications\n## Detection performance and validation\n# Conclusion","[{\"question\":\"What problem does OralNet address in oral cancer detection?\",\"answer\":\"OralNet targets timely detection of oral squamous cell carcinoma using histopathology images, supporting early diagnosis and clinical follow-up decisions.\"},{\"question\":\"How is OralNet structured in terms of processing stages?\",\"answer\":\"OralNet follows four stages: image collection and preprocessing, deep-and-handcrafted feature extraction, feature reduction with AHA plus serial concatenation, and binary classification with three-fold cross-validation.\"},{\"question\":\"What experimental setup and performance does the study report?\",\"answer\":\"The framework was tested on whole slide biopsy images at 100× and 400×, using 3000 cropped and resized images (1500 healthy, 1500 OSCC). Results show oral cancer detection accuracy exceeding 99.5%.\"}]","OralNet: Fused Optimal Deep Features Framework for Oral Squamous Cell Carcinoma Detection | PDF",1790228207]