[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-345428-105":59,"doc-detail-345428-en":134},{"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":127,"head_meta":129,"extra_data":131,"updated_unix":133},105,"en","modelling-of-hybrid-deep-ensemble-learning-based-skin-lesion-detection-using-fcnn-denoising-and-inception-dilated-resnetv2","Modelling of hybrid deep ensemble learning based skin lesion detection using FCNN denoising and inception-dilated ResNetV2","","Skin lesion classification supports computer-aided diagnosis for melanoma, where precise recognition is critical as global incidence rises. The study proposes IRLFE-SLC for medical imaging by combining FCNN-based denoising with an Inception-dilated ResNetV2 feature extractor to capture multi-scale hierarchical lesion characteristics. An ensemble of three deep learning models—Bi-LSTM, deep belief networks, and spiking neural networks—is used for classification. Results on the ISIC dataset show 99.06% accuracy and 95.68% macro-averaged multi-class AUC under an 80:20 split.",{"@graph":69,"@context":126},[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":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/modelling-of-hybrid-deep-ensemble-learning-based-skin-lesion-detection-using-fcnn-denoising-and-inception-dilated-resnetv2/345428/",{"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/modelling-of-hybrid-deep-ensemble-learning-based-skin-lesion-detection-using-fcnn-denoising-and-inception-dilated-resnetv2/345428.png","ImageObject",300,407,{"name":92,"@type":93},"Asher","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-09-25","2026-09-22",true,{"@type":102,"interactionType":103,"userInteractionCount":81},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118,122],{"name":109,"@type":110,"acceptedAnswer":111},"What problem does the document address in melanoma analysis?","Question",{"text":112,"@type":113},"It addresses the challenge of automatically classifying skin lesions for melanoma using medical imaging, where accurate diagnosis is difficult and existing clinician-based methods can be subjective and error-prone.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How does the proposed IRLFE-SLC approach process dermoscopic images?",{"text":117,"@type":113},"It first applies a FCNN denoising model to remove noise, then uses an Inception-dilated ResNetV2 model to extract multi-scale hierarchical features for lesion characterization.",{"name":119,"@type":110,"acceptedAnswer":120},"Which models are combined in the ensemble for classification?",{"text":121,"@type":113},"The ensemble uses three deep learning models: Bi-LSTM, deep belief networks (DBN), and spiking neural networks (SNN).",{"name":123,"@type":110,"acceptedAnswer":124},"What performance results are reported on the ISIC dataset?",{"text":125,"@type":113},"Under an 80:20 split on the ISIC dataset, IRLFE-SLC reports 99.06% accuracy and a 95.68% macro-averaged multi-class AUC, outperforming existing models in the comparison study.","https://schema.org",{"og:url":83,"og:type":128,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":130,"canonical":83},"index,follow",{"doc_id":132,"site_id":62},345428,1790101644,{"code":4,"msg":5,"data":135},{"doc_id":132,"user_id":136,"nickname":92,"user_avatar":137,"doc_module":4,"category_id":34,"category_name":35,"doc_title":65,"doc_description":67,"doc_content":138,"file_id":139,"file_url":140,"file_type":141,"file_size":142,"view_count":81,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":143,"language":144,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":145,"faqs":146,"seo_title":147,"seo_description":67,"update_tm":148,"read_time":149},687197207639,"https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd","[www. nature.com/scientificreports](www. nature.com/scientificreports)  \nOPEN  \nModelling of hybrid deep ensemble learning based skin lesion detection using FCNN denoising and inception-dilated ResNetV2  \nJaya Prakash Sunkavalli1, Denis Pustokhin2, E. Laxmi Lydia3, B. Prameela Rani4, Srijana Acharya5, Bhanu Shrestha6􀀍 & Cheol Lee7􀀍  \nSpecific metabolic and genetic abnormalities can cause skin lesions with cancerous potential. Cancerous cells might be spreading to every part of the body, whereas cancer is critical. Skin cancer is one of the prevalent cancers, and its global incidence continues to rise. Skin lesion classification is a significant stage in computer-aided diagnosis (CAD) for automatic analysis of melanoma. Recently, increased focus has been given to the deep learning (DL) methods applied for image analysis owing to their capability to utilise machine learning (ML) methods to convert input data into higher-level performance. Owing to precise diagnosis, the healthcare domain has a constantly rising interest in this technology, particularly in the analysis of melanoma. In this study, an integration of Residual Learning and Feature Extraction for Skin Lesion Classification (IRLFE-SLC) method is proposed for medical imaging. Initially, a fully convolutional neural network (FCNN) model is used for image denoising to effectively extract noise from dermoscopic images. Next, the feature extraction mechanism is employed using an Inception-dilated ResNetv2 model to capture multi-scale and hierarchical features critical for lesion characterisation. For the skin lesion classification, an ensemble of three DL models, such as bidirectional long short-term memory (Bi-LSTM), deep belief networks (DBN), and spiking neural networks (SNN) models, is utilised. To show the improved performance of the IRLFE-SLC methodology, a comprehensive investigational analysis is conducted. The comparison study of the IRLFE-SLC approach showed a superior accuracy of 99.06% and muti-class AUC score of 95.68%(macro-averaged) compared with existing models under an 80:20 split on the International Skin Imaging Collaboration (ISIC) skin cancer dataset.  \nKeywords Skin lesion, Ensemble learning, Deep belief networks, Spiking neural networks, Bidirectional Long Short-Term Memory, International skin imaging collaboration  \nMelanoma is a form of skin cancer recognised as one of the most lethal types of cutaneous malignancies, known for its ability to spread rapidly throughout the body1. Based on the statistical information given by the International Agency for Research and the World Health Organisation (WHO) on Cancer, according to the mission Globocan, the worldwide occurrence of melanoma is increasing progressively. Melanoma typically looks like an irregular mole2. Melanoma has arisen from a pre-existing mole that has altered in appearance, from a newly developed mole, or it may also emerge on another type of skin mark, or even on an area of skin with no visible mark at all. Further developed lesions might show ulceration, inflammation, bleeding, or itching3. However, certain melanomas don’t have the usual mole colour. It may also be smaller than 5 mm, whereas moles are usually larger than 5 mm. It may arise in parts that are certainly not exposed to sunlight. In this stage, the  \n1Department of Information Technology, Siddhartha Academy of Higher Education, Deemed to be University, Vijayawada 520007, Andhra Pradesh, India. 2Financial University under the Government of the Russian Federation, Moscow, Russia. 3Department of Computer Science and Engineering, Vignan’s Institute of Engineering for Women, Visakhapatnam 530046, Andhra Pradesh, India. 4Department of Computer Applications, Aditya University, Surampalem, India. 5College of Global Business, Kyungsung University, Busan, Republic of Korea. 6Department of Information Convergence System, Graduate School of Smart Convergence, Kwangwoon University, Seoul, Korea. 7Department of Smart Electrical and Electro","cbCaijOoXqDBG97h","https://ap.wps.com/l/cbCaijOoXqDBG97h","pdf",5062083,22,"English","# Introduction\n## Skin lesion classification and melanoma context\n## Dermoscopy and the need for automated CAD\n# Proposed IRLFE-SLC methodology\n## FCNN denoising for dermoscopic images\n## Inception-dilated ResNetV2 feature extraction\n## Ensemble classification using Bi-LSTM, DBN, and SNN\n# Experimental evaluation\n## Comparison results on the ISIC dataset\n## Reported accuracy and AUC metrics","[{\"question\":\"What problem does the document address in melanoma analysis?\",\"answer\":\"It addresses the challenge of automatically classifying skin lesions for melanoma using medical imaging, where accurate diagnosis is difficult and existing clinician-based methods can be subjective and error-prone.\"},{\"question\":\"How does the proposed IRLFE-SLC approach process dermoscopic images?\",\"answer\":\"It first applies a FCNN denoising model to remove noise, then uses an Inception-dilated ResNetV2 model to extract multi-scale hierarchical features for lesion characterization.\"},{\"question\":\"Which models are combined in the ensemble for classification?\",\"answer\":\"The ensemble uses three deep learning models: Bi-LSTM, deep belief networks (DBN), and spiking neural networks (SNN).\"},{\"question\":\"What performance results are reported on the ISIC dataset?\",\"answer\":\"Under an 80:20 split on the ISIC dataset, IRLFE-SLC reports 99.06% accuracy and a 95.68% macro-averaged multi-class AUC, outperforming existing models in the comparison study.\"}]","Modelling of hybrid deep ensemble learning based skin lesion detection using FCNN denoising and inception-dilated ResNetV2 | PDF",1790057640,55]