[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-328633-105":59,"doc-detail-328633-en":130},{"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":123,"head_meta":125,"extra_data":127,"updated_unix":129},105,"en","towards-laryngeal-cancer-diagnosis-using-dandelion-optimizer-algorithm-with-ensemble-learning-on-biomedical-throat-region-images","Towards laryngeal cancer diagnosis using Dandelion Optimizer Algorithm with ensemble learning on biomedical throat region images","","Laryngeal cancer is a significant global health burden where late-stage detection limits survival, making accurate computer-aided diagnosis essential. This work presents an automated laryngeal cancer diagnosis pipeline (LCD-DOAEL) for biomedical throat-region images, combining Gaussian filtering for noise removal, MobileNetV2 for feature extraction, and Dandelion Optimizer Algorithm-based hyperparameter selection. Classification is performed via an ensemble of BiLSTM, regularized ELM, and BPNN.",{"@graph":69,"@context":122},[70,84,105],{"@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":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/towards-laryngeal-cancer-diagnosis-using-dandelion-optimizer-algorithm-with-ensemble-learning-on-biomedical-throat-region-images/328633/",{"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/towards-laryngeal-cancer-diagnosis-using-dandelion-optimizer-algorithm-with-ensemble-learning-on-biomedical-throat-region-images/328633.png","ImageObject",300,407,{"name":92,"@type":93},"Asher","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-11-10","2026-09-21",true,{"@type":102,"interactionType":103,"userInteractionCount":39},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What is the LCD-DOAEL method designed to do?","Question",{"text":112,"@type":113},"LCD-DOAEL is designed to investigate biomedical throat-region images to detect the presence of laryngeal cancer.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How does the method prepare and extract features from the images?",{"text":117,"@type":113},"It applies Gaussian filtering to remove noise and uses MobileNetV2 to extract complex intrinsic feature patterns.",{"name":119,"@type":110,"acceptedAnswer":120},"Which models are used in the ensemble classifier and what performance is reported?",{"text":121,"@type":113},"The ensemble combines BiLSTM, regularized ELM, and BPNN classifiers, achieving reported accuracy of 97.54%.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},328633,1790196299,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":39,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":56,"language":139,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":140,"faqs":141,"seo_title":142,"seo_description":67,"update_tm":143,"read_time":144,"preview_page_count":4},687197207639,"https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd","[www. nature.com/scientificreports](www. nature.com/scientificreports)  \nOPEN  \nTowards laryngeal cancer diagnosis using Dandelion Optimizer Algorithm with ensemble learning on biomedical throat region images  \nSarahA. Alzakari1, Mashael Maashi2, SaadAlahmari3*, MunyaA. Arasi4, AbeerA. K. Alharbi 5 & Ahmed Sayed6  \nLaryngeal cancer exhibits a notable global health burden, with later-stage detection contributing toa low mortality rate. Laryngeal cancer diagnosis on throat region images is a pivotal application of computer vision (CV) and medical image diagnoses in the medical sector. It includes detecting and analysing abnormal or cancerous tissue from the larynx, an integral part of the vocal and respiratory systems. The computer-aided system makes use of artificial intelligence (AI) through deep learning (DL) and machine learning (ML) models, including convolution neural networks (CNN), for automated disease diagnoses and detection. Various DL and ML approaches are executed to categorize the extraction feature as healthy and cancerous tissues. This article introduces an automated Laryngeal Cancer Diagnosis using the Dandelion Optimizer Algorithm with Ensemble Learning (LCD-DOAEL) method on Biomedical Throat Region Image. The LCD-DOAEL method aims to investigate the images of the throat region for the presence of laryngeal cancer. In the LCD-DOAEL method, the Gaussian filtering (GF) approach is applied to eliminate the noise in the biomedical images. Besides, the complex and intrinsic feature patterns can be extracted by the MobileNetv2 model. Meanwhile, the DOA model carries out the hyperparameter selection of MobileNetV2 architecture. Finally, the ensemble of three classifiers such as bidirectional long short-term memory (BiLSTM), regularized extreme learning machine (ELM), and backpropagation neural network (BPNN) models, are utilized for the classification process. A comprehensive set of simulations is conducted on the biomedical image dataset to highlight the efficient performance of the LCD-DOAEL technique. The comparison analysis of the LCD-DOAEL method exhibited a superior accuracy outcome of 97.54% over other existing techniques.  \nKeywords Laryngeal cancer, Biomedical image, Dandelion Optimizer Algorithm, Ensemble learning, Narrow-band imaging  \nLaryngeal cancer indicates nearly 1% to 2% of cancers globally are rising while the incidence is reducing in a few states. The uppermost incidence is in Europe, while the percentage of death and incidence peaks in Africa1. Hypopharyngeal cancer is nearly 0.5% to 1%, with a noted rising rate in women in numerous states. Above 95% of hypopharyngeal and laryngeal cancers are squamous cell carcinoma (SCC), affected mainly through alcohol and tobacco intake2. Compared to oropharyngeal cancer, disease with the human papillomavirus does not play an efficient part3,4. In modern diagnosis, false cancer detection also exists, which is frequently related to the natural anxiety of experts to prevent overlooking cancer at initial stages5. To help in this method, researchers attempt to enhance pre-treatment estimates and offer intraoperative data on the pathologies. To get the histopathology of  \n1Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, 11671 Riyadh, Saudi Arabia. 2Department of Software Engineering, College of Computer and Information Sciences, King Saud University, Po Box 103786, 11543 Riyadh, Saudi Arabia. 3Department of Computer Science, Applied College, Northern Border University, Arar, Saudi Arabia. 4Department of Computer Science, Applied College at RijalAlmaa, King Khalid University, Abha, Saudi Arabia. 5Department Information Systems, College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), 11432 Riyadh, Saudi Arabia. 6Research Center, Future University in Egypt, New Cairo 11835, Egypt.*[email: Saad.alahmari@nbu.edu.sa](email: Saad.alahmari@nb","cbCaisYhJFJgKuAo","https://ap.wps.com/l/cbCaisYhJFJgKuAo","pdf",3590525,"English","# Method Overview\n## Preprocessing and Feature Extraction\n## Hyperparameter Selection\n## Ensemble Classification and Evaluation","[{\"question\":\"What is the LCD-DOAEL method designed to do?\",\"answer\":\"LCD-DOAEL is designed to investigate biomedical throat-region images to detect the presence of laryngeal cancer.\"},{\"question\":\"How does the method prepare and extract features from the images?\",\"answer\":\"It applies Gaussian filtering to remove noise and uses MobileNetV2 to extract complex intrinsic feature patterns.\"},{\"question\":\"Which models are used in the ensemble classifier and what performance is reported?\",\"answer\":\"The ensemble combines BiLSTM, regularized ELM, and BPNN classifiers, achieving reported accuracy of 97.54%.\"}]","Towards laryngeal cancer diagnosis using Dandelion Optimizer Algorithm with ensemble learning on biomedical throat region images | PDF",1789980457,48]