{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T16:05:04Z","timestamp":1774541104593,"version":"3.50.1"},"reference-count":100,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,10,17]],"date-time":"2025-10-17T00:00:00Z","timestamp":1760659200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Digit. Health"],"abstract":"<jats:sec><jats:title>Introduction<\/jats:title><jats:p>Early skin disease diagnosis is essential and one of the challenging tasks for a dermatologist. Manual diagnosis by healthcare providers is subjective, costly, and may yield inconsistent results. In contrast, automated skin disease detection and classification using traditional machine learning and deep learning approaches have shown promise in addressing this problem.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>In this study, we propose a hybrid ensemble framework that integrates both feature-level fusion (FLF) and decision-level fusion (DLF) to leverage complementary strengths for detecting and classifying skin diseases. We employ two convolutional neural network (CNN)-based models, i.e., a modified DenseNet201 and VGG19, along with an attention-based model vision transformer (ViT) to identify and classify skin diseases. In FLF, feature representations from these models are point-wise added and passed through a shared classification head to make the final prediction. In DLF, decisions from each base model are collected, and the majority voting scheme is used to make a final decision. Furthermore, we incorporate a generative adversarial network (GAN)-based approach for offline-based training data augmentation to reduce overfitting and improve performance.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Based on different evaluation metrics (i.e., accuracy, precision, recall, and F1-score), our proposed framework demonstrates superior performance on four benchmark datasets: the PH2, HAM10000, ISIC 2018, and ISIC 2019 datasets, with an accuracy of 99.3%\/99.2%, 92.7%\/96.1%, 86.7%\/89.0%, and 94.5%\/95.0%, respectively, for FLF\/DLF.<\/jats:p><\/jats:sec><jats:sec><jats:title>Discussion<\/jats:title><jats:p>These results demonstrate that while both fusion strategies are effective, DLF slightly outperforms FLF, emphasizing the value of ensemble decision aggregation for robust skin disease classification.<\/jats:p><\/jats:sec>","DOI":"10.3389\/fdgth.2025.1478688","type":"journal-article","created":{"date-parts":[[2025,10,17]],"date-time":"2025-10-17T05:29:55Z","timestamp":1760678995000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Skin disease diagnosis using decision and feature level fusion of deep features"],"prefix":"10.3389","volume":"7","author":[{"given":"Md.","family":"Zasim Uddin","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Md.","family":"Arif Shahriar","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bj\u00f6rn W.","family":"Schuller","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Md.","family":"Nadim Mahamood","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Md.","family":"Atiqur Rahman Ahad","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1965","published-online":{"date-parts":[[2025,10,17]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"364","DOI":"10.1016\/j.neucom.2021.08.096","article-title":"Skin disease diagnosis with deep learning: a review","volume":"464","author":"Li","year":"2021","journal-title":"Neurocomputing"},{"key":"B2","doi-asserted-by":"publisher","first-page":"738","DOI":"10.1038\/s41591-023-02225-7","article-title":"A deep-learning algorithm to classify skin lesions from mpox virus infection","volume":"29","author":"Thieme","year":"2023","journal-title":"Nat Med"},{"key":"B3","doi-asserted-by":"publisher","first-page":"4267","DOI":"10.1109\/JBHI.2021.3067789","article-title":"Multi-class skin lesion detection and classification via teledermatology","volume":"25","author":"Khan","year":"2021","journal-title":"IEEE J Biomed Health Inform"},{"key":"B4","article-title":"Skinsam: Empowering skin cancer segmentation with segment anything model","author":"Hu","year":""},{"key":"B5","doi-asserted-by":"publisher","first-page":"1366","DOI":"10.1080\/15384047.2019.1640032","article-title":"Current state of melanoma diagnosis and treatment","volume":"20","author":"Davis","year":"2019","journal-title":"Cancer Biol Ther"},{"key":"B6","first-page":"7","article-title":"Cancer statistics, 2019","volume":"69","author":"Siegel","year":"2019","journal-title":"CA Cancer J Clin"},{"key":"B7","doi-asserted-by":"publisher","first-page":"237","DOI":"10.1016\/j.bspc.2017.07.010","article-title":"Techniques and algorithms for computer aided diagnosis of pigmented skin lesions\u2014a review","volume":"39","author":"Pathan","year":"2018","journal-title":"Biomed Signal Process Control"},{"key":"B8","doi-asserted-by":"publisher","first-page":"101581","DOI":"10.1016\/j.bspc.2019.101581","article-title":"Extraction of features from cross correlation in space and frequency domains for classification of skin lesions","volume":"53","author":"Chatterjee","year":"2019","journal-title":"Biomed Signal Process Control"},{"key":"B9","doi-asserted-by":"publisher","first-page":"362","DOI":"10.1016\/j.compmedimag.2007.01.003","article-title":"A methodological approach to the classification of dermoscopy images","volume":"31","author":"Celebi","year":"2007","journal-title":"Comput Med Imaging Graph"},{"key":"B10","doi-asserted-by":"crossref","first-page":"712","DOI":"10.1111\/j.1365-4632.2008.03556.x","article-title":"Dermoscopy: basic concepts","volume":"47","author":"Campos-do Carmo","year":"2008","journal-title":"Int J Dermatol"},{"key":"B11","doi-asserted-by":"publisher","first-page":"734906","DOI":"10.1155\/2014\/734906","article-title":"Skin biopsy in the context of dermatological diagnosis: a retrospective cohort study","volume":"2014","author":"Korfitis","year":"2014","journal-title":"Dermatol Res Pract"},{"key":"B12","doi-asserted-by":"publisher","first-page":"4008","DOI":"10.3390\/s22114008","article-title":"An effective skin cancer classification mechanism via medical vision transformer","volume":"22","author":"Aladhadh","year":"2022","journal-title":"Sensors"},{"key":"B13","doi-asserted-by":"crossref","DOI":"10.1088\/1742-6596\/2405\/1\/012037","article-title":"Skin cancer classification based on convolutional neural networks and vision transformers","author":"Zhao","year":""},{"key":"B14","doi-asserted-by":"publisher","first-page":"108798","DOI":"10.1016\/j.compbiomed.2024.108798","article-title":"Skin-cad: explainable deep learning classification of skin cancer from dermoscopic images by feature selection of dual high-level cnns features and transfer learning","volume":"178","author":"Attallah","year":"2024","journal-title":"Comput Biol Med"},{"key":"B15","doi-asserted-by":"publisher","first-page":"1342","DOI":"10.3390\/electronics12061342","article-title":"A deep-ensemble-learning-based approach for skin cancer diagnosis","volume":"12","author":"Shehzad","year":"2023","journal-title":"Electronics"},{"key":"B16","doi-asserted-by":"publisher","first-page":"100122","DOI":"10.1016\/j.health.2022.100122","article-title":"A machine learning approach for skin disease detection and classification using image segmentation","volume":"2","author":"Ahammed","year":"2022","journal-title":"Healthc Anal"},{"key":"B17","first-page":"997","article-title":"Advance study of skin diseases detection using image processing methods","volume":"9","author":"Jagdish","year":"2022","journal-title":"Nat Volatiles Essent OILS J"},{"key":"B18","first-page":"1","article-title":"Multichannel deep attention neural networks for the classification of autism spectrum disorder using neuroimaging and personal characteristic data","volume":"2020","author":"Niu","year":"2020","journal-title":"Complexity"},{"key":"B19","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1016\/j.ejca.2019.05.023","article-title":"Deep neural networks are superior to dermatologists in melanoma image classification","volume":"119","author":"Brinker","year":"2019","journal-title":"Eur J Cancer"},{"key":"B20","doi-asserted-by":"publisher","first-page":"e12497","DOI":"10.1111\/exsy.12497","article-title":"Skin lesion segmentation and classification: a unified framework of deep neural network features fusion and selection","volume":"39","author":"Khan","year":"2022","journal-title":"Expert Syst"},{"key":"B21","doi-asserted-by":"publisher","first-page":"105151","DOI":"10.1016\/j.engappai.2022.105151","article-title":"Ensemble deep learning: a review","volume":"115","author":"Ganaie","year":"2022","journal-title":"Eng Appl Artif Intell"},{"key":"B22","doi-asserted-by":"publisher","first-page":"107154","DOI":"10.1016\/j.compbiomed.2023.107154","article-title":"An efficient artificial rabbits optimization based on mutation strategy for skin cancer prediction","volume":"163","author":"Abd Elaziz","year":"2023","journal-title":"Comput Biol Med"},{"key":"B23","first-page":"1","article-title":"The ou-isir large population gait database with real-life carried object and its performance evaluation","volume":"10","author":"Uddin","year":"2018","journal-title":"IPSJ Trans Comput Vision Appl"},{"key":"B24","first-page":"1","article-title":"Spatio-temporal silhouette sequence reconstruction for gait recognition against occlusion","volume":"11","author":"Uddin","year":"2019","journal-title":"IPSJ Trans Comput Vis Appl"},{"key":"B25","doi-asserted-by":"publisher","first-page":"103074","DOI":"10.1016\/j.micpro.2020.103074","article-title":"Automatic diagnosis of skin diseases using convolution neural network","volume":"76","author":"Shanthi","year":"2020","journal-title":"Microprocess Microsyst"},{"key":"B26","first-page":"3145","article-title":"Deep learning based automated diagnosis of skin diseases using dermoscopy","volume":"71","author":"Anand","year":"2022","journal-title":"Comput Mater Contin"},{"key":"B27","doi-asserted-by":"publisher","first-page":"e81","DOI":"10.1002\/ski2.81","article-title":"Automatic skin disease diagnosis using deep learning from clinical image and patient information","volume":"2","author":"Muhaba","year":"2022","journal-title":"Skin Health Dis"},{"key":"B28","doi-asserted-by":"publisher","first-page":"104779","DOI":"10.1016\/j.bspc.2023.104779","article-title":"Oesv-krf: optimal ensemble support vector kernel random forest based early detection and classification of skin diseases","volume":"85","author":"Kalpana","year":"2023","journal-title":"Biomed Signal Process Control"},{"key":"B29","doi-asserted-by":"crossref","DOI":"10.1109\/SKIMA.2018.8631525","article-title":"Multi-class skin diseases classification using deep convolutional neural network and support vector machine","author":"Hameed","year":""},{"key":"B30","doi-asserted-by":"publisher","first-page":"1294","DOI":"10.3390\/electronics11091294","article-title":"Deep learning methods for accurate skin cancer recognition and mobile application","volume":"11","author":"Kousis","year":"2022","journal-title":"Electronics"},{"key":"B31","first-page":"323","article-title":"A fine tuning approach using modified densenet model for skin cancer classification","volume":"15","author":"Mondal","year":"2023","journal-title":"Int J Med Eng Inform"},{"key":"B32","doi-asserted-by":"publisher","first-page":"37379","DOI":"10.1007\/s11042-021-11628-y","article-title":"An automated deep learning models for classification of skin disease using dermoscopy images: a comprehensive study","volume":"81","author":"Anand","year":"2022","journal-title":"Multimed Tools Appl"},{"key":"B33","doi-asserted-by":"crossref","first-page":"103406","DOI":"10.1016\/j.bspc.2021.103406","article-title":"Eff2net: an efficient channel attention-based convolutional neural network for skin disease classification","volume":"73","author":"Vaichole","year":"2022","journal-title":"Biomed Signal Process Control"},{"key":"B34","doi-asserted-by":"publisher","first-page":"8943","DOI":"10.1007\/s12652-022-04400-z","article-title":"Automatic skin lesion classification using a novel densely connected convolutional network integrated with an attention module","volume":"14","author":"Shan","year":"2023","journal-title":"J Ambient Intell Humaniz Comput"},{"key":"B35","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s10278-023-00862-5","article-title":"Deep learning\u2013based skin lesion multi-class classification with global average pooling improvement","volume":"36","author":"Raghavendra","year":"2023","journal-title":"J Digit Imaging"},{"key":"B36","doi-asserted-by":"publisher","first-page":"2852","DOI":"10.3390\/s21082852","article-title":"Classification of skin disease using deep learning neural networks with mobilenet v2 and lstm","volume":"21","author":"Srinivasu","year":"2021","journal-title":"Sensors"},{"key":"B37","doi-asserted-by":"publisher","first-page":"1006","DOI":"10.1109\/TBME.2018.2866166","article-title":"Melanoma recognition in dermoscopy images via aggregated deep convolutional features","volume":"66","author":"Yu","year":"2018","journal-title":"IEEE Trans Biomed Eng"},{"key":"B38","doi-asserted-by":"crossref","first-page":"1555","DOI":"10.31557\/APJCP.2019.20.5.1555","article-title":"Deep learning based skin lesion segmentation and classification of melanoma using support vector machine (svm)","volume":"20","author":"Seeja","year":"2019","journal-title":"Asian Pac J Cancer Prev"},{"key":"B39","doi-asserted-by":"publisher","first-page":"11","DOI":"10.14445\/22315381\/IJETT-V70I2P202","article-title":"Machine learning and deep learning integration for skin diseases prediction","volume":"70","author":"Bandyopadhyay","year":"2022","journal-title":"Int J Eng Trends Technol"},{"key":"B40","doi-asserted-by":"crossref","first-page":"5350","DOI":"10.1038\/s41598-021-84593-z","article-title":"Ai-based localization and classification of skin disease with erythema","volume":"11","author":"Min Son","year":"2021","journal-title":"Sci Rep"},{"key":"B41","doi-asserted-by":"publisher","first-page":"717","DOI":"10.1007\/s10619-021-07360-z","article-title":"Deep learning-based computer aided diagnosis model for skin cancer detection and classification","volume":"40","author":"Adla","year":"2022","journal-title":"Distrib Parallel Database"},{"key":"B42","doi-asserted-by":"publisher","first-page":"102391","DOI":"10.1016\/j.eclinm.2023.102391","article-title":"A deep learning fusion network trained with clinical and high-frequency ultrasound images in the multi-classification of skin diseases in comparison with dermatologists: a prospective and multicenter study","volume":"67","author":"Zhu","year":"2024","journal-title":"eClinicalMedicine"},{"key":"B43","volume-title":"Deep Learning","author":"Goodfellow","year":"2016"},{"key":"B44","article-title":"An image is worth 16\u00d716 words: transformers for image recognition at scale","author":"Dosovitskiy","year":""},{"key":"B45","doi-asserted-by":"crossref","DOI":"10.1109\/ISBI56570.2024.10635300","article-title":"Sosegformer: a cross-scale feature correlated network for small medical object segmentation","author":"Dai","year":""},{"key":"B46","doi-asserted-by":"publisher","first-page":"3960","DOI":"10.1109\/TASE.2024.3404488","article-title":"Automated non-invasive analysis of motile sperms using sperm feature-correlated network","volume":"22","author":"Dai","year":"2024","journal-title":"IEEE Trans Autom Sci Eng"},{"key":"B47","doi-asserted-by":"publisher","first-page":"105939","DOI":"10.1016\/j.compbiomed.2022.105939","article-title":"An improved transformer network for skin cancer classification","volume":"149","author":"Xin","year":"2022","journal-title":"Comput Biol Med"},{"key":"B48","doi-asserted-by":"publisher","first-page":"719","DOI":"10.1109\/JBHI.2023.3308697","article-title":"Deeply supervised skin lesions diagnosis with stage and branch attention","volume":"28","author":"Dai","year":"2023","journal-title":"IEEE J Biomed Health Inform"},{"key":"B49","doi-asserted-by":"publisher","first-page":"72","DOI":"10.3390\/diagnostics13010072","article-title":"A deep cnn transformer hybrid model for skin lesion classification of dermoscopic images using focal loss","volume":"13","author":"Nie","year":"2023","journal-title":"Diagnostics"},{"key":"B50","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1155\/2023\/5146543","article-title":"Multiscale feature fusion for skin lesion classification","volume":"2023","author":"Wang","year":"2023","journal-title":"Biomed Res Int"},{"key":"B51","doi-asserted-by":"publisher","first-page":"419","DOI":"10.1007\/s11042-024-18958-7","article-title":"Multi-feature fusion deep network for skin disease diagnosis","volume":"84","author":"Gairola","year":"2025","journal-title":"Multimed Tools Appl"},{"key":"B52","doi-asserted-by":"publisher","first-page":"103729","DOI":"10.1016\/j.bspc.2022.103729","article-title":"Ensemble of weighted deep concatenated features for the skin disease classification model using modified long short term memory","volume":"76","author":"Elashiri","year":"2022","journal-title":"Biomed Signal Process Control"},{"key":"B53","doi-asserted-by":"publisher","first-page":"799","DOI":"10.3390\/s22030799","article-title":"Multiclass skin lesion classification using hybrid deep features selection and extreme learning machine","volume":"22","author":"Afza","year":"2022","journal-title":"Sensors"},{"key":"B54","doi-asserted-by":"publisher","first-page":"758495","DOI":"10.3389\/fbioe.2021.758495","article-title":"Two-stage deep neural network via ensemble learning for melanoma classification","volume":"9","author":"Ding","year":"2022","journal-title":"Front Bioeng Biotechnol"},{"key":"B55","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1016\/j.jbi.2018.08.006","article-title":"Skin lesion classification with ensembles of deep convolutional neural networks","volume":"86","author":"Harangi","year":"2018","journal-title":"J Biomed Inform"},{"key":"B56","doi-asserted-by":"crossref","DOI":"10.1109\/CVPR.2017.243","article-title":"Densely connected convolutional networks","author":"Huang","year":""},{"key":"B57","article-title":"Very deep convolutional networks for large-scale image recognition","author":"Simonyan","year":""},{"key":"B58","doi-asserted-by":"publisher","first-page":"49080","DOI":"10.1109\/ACCESS.2018.2865544","article-title":"Dense convolutional binary-tree networks for lung nodule classification","volume":"6","author":"Liu","year":"2018","journal-title":"IEEE Access"},{"key":"B59","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1002\/ima.22812","article-title":"A deep learning approach for classification of covid and pneumonia using densenet-201","volume":"33","author":"Sanghvi","year":"2023","journal-title":"Int J Imaging Syst Technol"},{"key":"B60","first-page":"6000","article-title":"Attention is all you need","volume":"30","author":"Vaswani","year":"2017","journal-title":"Adv Neural Inf Process Syst"},{"key":"B61","article-title":"Ph 2-a dermoscopic image database for research and benchmarking","author":"Mendon\u00e7a","year":""},{"key":"B62","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/sdata.2018.161","article-title":"The ham10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions","volume":"5","author":"Tschandl","year":"2018","journal-title":"Sci Data"},{"key":"B63","doi-asserted-by":"publisher","first-page":"109347","DOI":"10.1016\/j.patcog.2023.109347","article-title":"A comprehensive survey of image augmentation techniques for deep learning","volume":"137","author":"Xu","year":"2023","journal-title":"Pattern Recognit"},{"key":"B64","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s40537-019-0197-0","article-title":"A survey on image data augmentation for deep learning","volume":"6","author":"Shorten","year":"2019","journal-title":"J Big Data"},{"key":"B65","first-page":"12104","article-title":"Training generative adversarial networks with limited data","volume":"33","author":"Karras","year":"2020","journal-title":"Adv Neural Inf Process Syst"},{"key":"B66","first-page":"6629","article-title":"Gans trained by a two time-scale update rule converge to a local nash equilibrium","volume":"30","author":"Heusel","year":"2017","journal-title":"Adv Neural Inf Process Syst"},{"key":"B67","doi-asserted-by":"publisher","first-page":"107185","DOI":"10.1016\/j.engappai.2023.107185","article-title":"Deep learning with image-based autism spectrum disorder analysis: a systematic review","volume":"127","author":"Uddin","year":"2024","journal-title":"Eng Appl Artif Intell"},{"key":"B68","doi-asserted-by":"publisher","first-page":"101701","DOI":"10.1016\/j.tice.2021.101701","article-title":"Multi-features extraction based on deep learning for skin lesion classification","volume":"74","author":"Benyahia","year":"2022","journal-title":"Tissue Cell"},{"key":"B69","doi-asserted-by":"publisher","first-page":"12394","DOI":"10.1007\/s11227-022-04371-0","article-title":"A hybrid deep learning approach for skin cancer diagnosis using subband fusion of 3D wavelets","volume":"78","author":"Maniraj","year":"2022","journal-title":"J Supercomput"},{"key":"B70","doi-asserted-by":"publisher","first-page":"88","DOI":"10.1016\/j.ymeth.2021.02.013","article-title":"A hierarchical three-step superpixels and deep learning framework for skin lesion classification","volume":"202","author":"Afza","year":"2022","journal-title":"Methods"},{"key":"B71","doi-asserted-by":"publisher","first-page":"274","DOI":"10.1016\/j.procs.2023.01.009","article-title":"A scheme for effective skin disease detection using optimized region growing segmentation and autoencoder based classification","volume":"218","author":"Reddy","year":"2023","journal-title":"Procedia Comput Sci"},{"key":"B72","doi-asserted-by":"publisher","first-page":"238","DOI":"10.1016\/j.neunet.2023.01.022","article-title":"Multiclass skin lesion localization and classification using deep learning based features fusion and selection framework for smart healthcare","volume":"160","author":"Maqsood","year":"2023","journal-title":"Neural Netw"},{"key":"B73","doi-asserted-by":"crossref","first-page":"e0315120","DOI":"10.1371\/journal.pone.0315120","article-title":"Deep learning-based skin lesion analysis using hybrid resunet++ and modified alexnet-random forest for enhanced segmentation and classification","volume":"20","author":"Mustafa","year":"2025","journal-title":"PLoS One"},{"key":"B74","doi-asserted-by":"publisher","first-page":"3429","DOI":"10.1109\/TMI.2020.2995518","article-title":"Semi-supervised medical image classification with relation-driven self-ensembling model","volume":"39","author":"Liu","year":"2020","journal-title":"IEEE Trans Med Imaging"},{"key":"B75","doi-asserted-by":"publisher","first-page":"105351","DOI":"10.1016\/j.cmpb.2020.105351","article-title":"Multiple skin lesions diagnostics via integrated deep convolutional networks for segmentation and classification","volume":"190","author":"Al-Masni","year":"2020","journal-title":"Comput Methods Programs Biomed"},{"key":"B76","first-page":"1","article-title":"A multimodal transformer to fuse images and metadata for skin disease classification","volume":"39","author":"Cai","year":"2022","journal-title":"Vis Comput"},{"key":"B77","first-page":"100034","article-title":"Multiclass skin cancer classification using efficientnets\u2013a first step towards preventing skin cancer","volume":"2","author":"Ali","year":"2022","journal-title":"Neurosci Inf"},{"key":"B78","doi-asserted-by":"publisher","first-page":"18134","DOI":"10.1038\/s41598-022-22644-9","article-title":"Skin lesion classification of dermoscopic images using machine learning and convolutional neural network","volume":"12","author":"Shetty","year":"2022","journal-title":"Sci Rep"},{"key":"B79","doi-asserted-by":"publisher","first-page":"2103","DOI":"10.3390\/electronics12092103","article-title":"A skin cancer classification method based on discrete wavelet down-sampling feature reconstruction","volume":"12","author":"Wu","year":"2023","journal-title":"Electronics"},{"key":"B80","doi-asserted-by":"publisher","first-page":"e0284437","DOI":"10.1371\/journal.pone.0284437","article-title":"Design and validation of a new machine-learning-based diagnostic tool for the differentiation of dermatoscopic skin cancer images","volume":"18","author":"Tajerian","year":"2023","journal-title":"PLoS One"},{"key":"B81","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s40747-023-01049-9","article-title":"A stereo spatial decoupling network for medical image classification","volume":"9","author":"You","year":"2023","journal-title":"Complex Intell Syst"},{"key":"B82","doi-asserted-by":"publisher","first-page":"438","DOI":"10.3390\/electronics12020438","article-title":"A skin disease classification model based on densenet and convnext fusion","volume":"12","author":"Wei","year":"2023","journal-title":"Electronics"},{"key":"B83","doi-asserted-by":"publisher","first-page":"107627","DOI":"10.1016\/j.bspc.2025.107627","article-title":"A novel cnn-vit-based deep learning model for early skin cancer diagnosis","volume":"104","author":"Pacal","year":"2025","journal-title":"Biomed Signal Process Control"},{"key":"B84","article-title":"Ensembling convolutional neural networks for skin cancer classification","author":"Nozdryn-Plotnicki","year":""},{"key":"B85","article-title":"Skin lesion diagnosis using ensembles, unscaled multi-crop evaluation and loss weighting","author":"Gessert","year":""},{"key":"B86","first-page":"1","article-title":"Skin lesion analysis towards melanoma detection using deep neural network ensemble","volume":"2018","author":"Zhuang","year":"2018","journal-title":"ISIC Chall"},{"key":"B87","doi-asserted-by":"publisher","first-page":"105475","DOI":"10.1016\/j.cmpb.2020.105475","article-title":"Transfer learning using a multi-scale and multi-network ensemble for skin lesion classification","volume":"193","author":"Mahbod","year":"2020","journal-title":"Comput Methods Programs Biomed"},{"key":"B88","doi-asserted-by":"publisher","first-page":"9765307","DOI":"10.34133\/2022\/9765307","article-title":"A low-cost high-performance data augmentation for deep learning-based skin lesion classification","volume":"2022","author":"Shen","year":"2022","journal-title":"BME Front"},{"key":"B89","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41591-023-02475-5","article-title":"A reinforcement learning model for AI-based decision support in skin cancer","volume":"29","author":"Barata","year":"2023","journal-title":"Nat Med"},{"key":"B90","doi-asserted-by":"publisher","first-page":"105068","DOI":"10.1016\/j.bspc.2023.105068","article-title":"Skin lesion classification based on multi-model ensemble with generated levels-of-detail images","volume":"85","author":"Tsai","year":"2023","journal-title":"Biomed Signal Process Control"},{"key":"B91","doi-asserted-by":"publisher","first-page":"114822","DOI":"10.1109\/ACCESS.2020.3003890","article-title":"Skin lesions classification into eight classes for ISIC 2019 using deep convolutional neural network and transfer learning","volume":"8","author":"Kassem","year":"2020","journal-title":"IEEE Access"},{"key":"B92","doi-asserted-by":"publisher","first-page":"100864","DOI":"10.1016\/j.mex.2020.100864","article-title":"Skin lesion classification using ensembles of multi-resolution efficientnets with meta data","volume":"7","author":"Gessert","year":"2020","journal-title":"MethodsX"},{"key":"B93","doi-asserted-by":"crossref","DOI":"10.1007\/978-981-15-8391-9_42","article-title":"Skin lesion classification using deep learning","author":"Bhardwaj","year":""},{"key":"B94","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s11042-021-11823-x","article-title":"Multi-type skin diseases classification using op-dnn based feature extraction approach","volume":"81","author":"Jain","year":"2022","journal-title":"Multimed Tools Appl"},{"key":"B95","doi-asserted-by":"publisher","first-page":"102693","DOI":"10.1016\/j.media.2022.102693","article-title":"Ssd-kd: a self-supervised diverse knowledge distillation method for lightweight skin lesion classification using dermoscopic images","volume":"84","author":"Wang","year":"2023","journal-title":"Med Image Anal"},{"key":"B96","doi-asserted-by":"publisher","first-page":"1123581","DOI":"10.3389\/fpubh.2023.1123581","article-title":"A novel approach toward skin cancer classification through fused deep features and neutrosophic environment","volume":"11","author":"Abdelhafeez","year":"2023","journal-title":"Front Public Health"},{"key":"B97","doi-asserted-by":"publisher","first-page":"3604","DOI":"10.3390\/cancers15143604","article-title":"Sbxception: a shallower and broader xception architecture for efficient classification of skin lesions","volume":"15","author":"Mehmood","year":"2023","journal-title":"Cancers"},{"key":"B98","doi-asserted-by":"crossref","DOI":"10.1109\/ICCV.2017.74","article-title":"Grad-cam: visual explanations from deep networks via gradient-based localization","author":"Selvaraju","year":""},{"key":"B99","doi-asserted-by":"publisher","first-page":"757","DOI":"10.1016\/j.jksuci.2023.01.014","article-title":"A comprehensive review on ensemble deep learning: opportunities and challenges","volume":"35","author":"Mohammed","year":"2023","journal-title":"J King Saud Univ Comput Inf Sci"},{"key":"B100","doi-asserted-by":"publisher","first-page":"538","DOI":"10.1109\/JBHI.2018.2824327","article-title":"Seven-point checklist and skin lesion classification using multitask multimodal neural nets","volume":"23","author":"Kawahara","year":"2018","journal-title":"IEEE J Biomed Health Inf"}],"container-title":["Frontiers in Digital Health"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fdgth.2025.1478688\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,17]],"date-time":"2025-10-17T05:30:00Z","timestamp":1760679000000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fdgth.2025.1478688\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,17]]},"references-count":100,"alternative-id":["10.3389\/fdgth.2025.1478688"],"URL":"https:\/\/doi.org\/10.3389\/fdgth.2025.1478688","relation":{},"ISSN":["2673-253X"],"issn-type":[{"value":"2673-253X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,17]]},"article-number":"1478688"}}