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HyperFusion-Net introduces a hybrid deep learning architecture that couples a MultiPath Vision Transformer with an attention U-Net to perform melanoma classification and precise lesion segmentation from dermoscopic images. Compared with CNN-based baselines, the model improves feature extraction and spatial precision via a mutual attention fusion block. Training and evaluation use four ISIC datasets with 60,000+ images, with preprocessing for robustness. Results show higher classification accuracy and segmentation quality, supported by ablation studies and cross-dataset generalizability.",{"@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":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/hyperfusionnet-combines-vision-transformer-for-early-melanoma-detection-and-precise-lesion-segmentation/450231/",{"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/hyperfusionnet-combines-vision-transformer-for-early-melanoma-detection-and-precise-lesion-segmentation/450231.png","ImageObject",300,407,{"name":92,"@type":93},"Theodore","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-07","2026-09-30",true,{"@type":102,"interactionType":103,"userInteractionCount":29},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What is the main goal of HyperFusion-Net?","Question",{"text":112,"@type":113},"HyperFusion-Net aims to simultaneously classify melanoma and segment lesions precisely in dermoscopic images using a hybrid transformer-U-Net design.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"Why are traditional melanoma diagnosis methods difficult for this task?",{"text":117,"@type":113},"Skin lesions vary widely in appearance, dermoscopic images may contain noise like hair and lighting artifacts, and conventional models often rely on hand-crafted features that fail to capture complex patterns.",{"name":119,"@type":110,"acceptedAnswer":120},"How does HyperFusion-Net improve over CNN-based approaches?",{"text":121,"@type":113},"It leverages transformer-based feature extraction for semantic understanding and uses an attention U-Net for spatially accurate segmentation, enhanced by a mutual attention fusion block to integrate semantic and spatial features.","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},450231,1791244019,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":34,"category_name":35,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":29,"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},7971461740886,"https://ap-avatar.wpscdn.com/davatar_3d24733baf745e90a7e4bdd5f77d97b2","[www. nature.com/scientificreports](www. nature.com/scientificreports)  \nOPEN  \nHyperFusionNet combines vision transformer for early melanoma detection and precise lesion segmentation  \nMin Li1, Yinping Jiang2, Ge Cao2, Tao Xu2 & Ruiqiang Guo3􀀍  \nEarly and accurate diagnosis of melanoma remains a major challenge duetothe heterogeneous nature of skin lesions and the limitations of traditional diagnostic tools. In this study, we introduce HyperFusion-Net, a novel hybrid deep learning architecture that synergistically integrates a MultiPath Vision Transformer (MPViT) and an attention U-Net to simultaneously perform melanoma classification and lesion segmentation in dermoscopic images. Unlike conventional CNN-based methods, HyperFusion-Net combines the general feature extraction capabilities of transducers with the spatial accuracy of the U-Net, which is enhanced by a mutual attention fusion block that facilitates the effective fusion of semantic and spatial features. The model was trained and evaluated using four public ISIC datasets containing over 60,000 dermoscopic images. Preprocessing techniques such as hair removal, clipping, and normalization were applied to improve robustness. Experimental results show that HyperFusion-Net consistently outperforms state-of-the-art models including  \nU-Net, DeepLabV3 +, TransUNet, and Swin-UNet, achieving superior performance in classification (accuracy: 93.24%, AUC: 95.80%) and segmentation (Dice coefficient: 0.945 in ISIC 2024). Ablation studies confirm the effectiveness of the multi-path design and fusion strategy in enhancing diagnostic performance while maintaining computational efficiency. Furthermore, the model demonstrates strong generalizability across datasets with different lesion types and imaging conditions.  \nKeywords Melanoma detection, Dermoscopic images, Vision transformer, Hybrid deep learning, Lesion segmentation, Skin cancer diagnosis, Medical image analysis  \nMelanoma, a type of skin cancer, continues to be one of the most lethal cancers globally, contributing substantially to cancer-related deaths1. The World Health Organization reported that cancer resulted in around 9.6 million fatalities in 2018, with skin cancer accounting for over 40% of identified cancer cases worldwide2. Melanoma, arising from irregularities in melanocyte cells that produce melanin, is especially deadly due to its swift metastatic capability, frequently disseminating to vital organs such as the brain3, liver, and lungs if not identified promptly4. Timely diagnosis and accurate lesion segmentation are crucial for enhancing patient outcomes, as prompt intervention can markedly improve the efficacy of treatments such as surgery or targeted medicines5. Dermoscopy, a non-invasive imaging modality, has emerged as a fundamental tool in the visual diagnosis of cutaneous diseases6, allowing dermatologists to discern malignant features that are imperceptible to the unaided eye7. Nonetheless, despite its prevalent application, the precision of dermoscopic diagnosis is significantly contingent upon the dermatologist’s ability, with research indicating that seasoned specialists (over 10 years) attain an accuracy of 80%, but those with 3–5 years of experience obtain just 62%8,9. This variety highlights the must for automated, dependable, and objective diagnostic instruments to aid clinicians in the early detection of melanoma and precise lesion segmentation10.  \nRecent advances in medical image analysis have also demonstrated the applicability of feature fusion and transformer-based modules for lesion detection. For example11, proposed an interactive transformer for skin lesion classification, while deep learning approaches have also been applied to ultrasound imaging for superresolution and noise removal tasks12, 13. Furthermore, studies on skin-related disorders, such as keloidal fibroblast analysis14, further emphasize the importance of robust image analysis frameworks in dermatology.  \n1The Keimyung Acad","cbCaie0BUDPPBeNP","https://ap.wps.com/l/cbCaie0BUDPPBeNP","pdf",2884985,"English","# Introduction\n## Challenges in dermoscopic melanoma diagnosis\n## Motivation for automated, objective tools\n# Related Work\n## Feature fusion and transformer-based lesion analysis\n## Prior approaches and limitations\n# Method Overview\n## HyperFusion-Net architecture for classification and segmentation\n# Experimental Setup and Evaluation\n## Datasets, preprocessing, and training\n## Performance metrics and comparisons\n# Results and Analysis\n## Ablation studies and efficiency\n## Generalizability across datasets","[{\"question\":\"What is the main goal of HyperFusion-Net?\",\"answer\":\"HyperFusion-Net aims to simultaneously classify melanoma and segment lesions precisely in dermoscopic images using a hybrid transformer-U-Net design.\"},{\"question\":\"Why are traditional melanoma diagnosis methods difficult for this task?\",\"answer\":\"Skin lesions vary widely in appearance, dermoscopic images may contain noise like hair and lighting artifacts, and conventional models often rely on hand-crafted features that fail to capture complex patterns.\"},{\"question\":\"How does HyperFusion-Net improve over CNN-based approaches?\",\"answer\":\"It leverages transformer-based feature extraction for semantic understanding and uses an attention U-Net for spatially accurate segmentation, enhanced by a mutual attention fusion block to integrate semantic and spatial features.\"}]","HyperFusionNet combines vision transformer for early melanoma detection and precise lesion segmentation | PDF",1790732579,48]