{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,29]],"date-time":"2026-10-29T17:41:20Z","timestamp":1793295680933,"version":"4.5.0"},"reference-count":41,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T00:00:00Z","timestamp":1773187200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T00:00:00Z","timestamp":1776816000000},"content-version":"vor","delay-in-days":42,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"name":"Vellore Institute of Technology- AP University"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci Rep"],"DOI":"10.1038\/s41598-026-43339-5","type":"journal-article","created":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T12:49:26Z","timestamp":1773233366000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Enhanced skin cancer classification for minority classes using Conditional GAN pipeline and CNN-ViT ensemble"],"prefix":"10.1038","volume":"16","author":[{"given":"Shaik Riyaz","family":"Hussain","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Saladi","family":"Saritha","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Abhi","family":"Chevuri","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yepuganti","family":"Karuna","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,3,11]]},"reference":[{"issue":"2","key":"43339_CR1","doi-asserted-by":"publisher","first-page":"185","DOI":"10.1097\/CM9.0000000000003416","volume":"138","author":"Wang","year":"2025","unstructured":"Wang Mingyue1,2,3,4; Gao, Xinghua1,2,3,4; Zhang, Li1,2,3,4. Recent global patterns in skin cancer incidence, mortality, and prevalence. Chin. Med. J. 138 (2), 185\u2013192. (2025). https:\/\/doi.org\/10.1097\/CM9.0000000000003416","journal-title":"Chin. Med. J."},{"key":"43339_CR2","unstructured":"World Cancer Research Fund. https:\/\/www.wcrf.org\/preventing-cancer\/cancer-statistics\/skin-cancer-statistics\/"},{"key":"43339_CR3","unstructured":"Sundararajan, S. et al. Metastatic Melanoma. [Updated 2024 Feb 17]. In: StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2025 Jan-. Available from: https:\/\/www.ncbi.nlm.nih.gov\/books\/NBK470358\/"},{"key":"43339_CR4","doi-asserted-by":"crossref","unstructured":"Khalaf, A. D., Hamdan, H., Halin, A. B. A. & Manshor, N. Segmentation and Classification of Skin Cancer Diseases Based on Deep Learning: Challenges and Future Directions. IEEE Access. (2025).","DOI":"10.1109\/ACCESS.2025.3569170"},{"issue":"13","key":"43339_CR5","doi-asserted-by":"publisher","first-page":"3731","DOI":"10.1049\/ipr2.13219","volume":"18","author":"D Meedeniya","year":"2024","unstructured":"Meedeniya, D., De Silva, S., Gamage, L. & Isuranga, U. Skin cancer identification utilizing deep learning: A survey. IET Image Proc. 18 (13), 3731\u20133749 (2024).","journal-title":"IET Image Proc."},{"key":"43339_CR6","doi-asserted-by":"publisher","first-page":"95716","DOI":"10.1109\/ACCESS.2022.3199613","volume":"10","author":"Y Nie","year":"2022","unstructured":"Nie, Y. et al. Recent Advances in Diagnosis of Skin Lesions Using Dermoscopic Images Based on Deep Learning. IEEE Access. 10, 95716\u201395747. https:\/\/doi.org\/10.1109\/ACCESS.2022.3199613 (2022).","journal-title":"IEEE Access."},{"key":"43339_CR7","doi-asserted-by":"publisher","first-page":"39045","DOI":"10.1109\/ACCESS.2022.3165574","volume":"10","author":"A. Mohanty","year":"2022","unstructured":"Mohanty, A., Sutherland, A., Bezbradica, M. & Javidnia, H. Skin disease analysis with limited data in particular rosacea: a review and recommended framework. IEEE Access. 10, 39045\u201339068. (2022). https:\/\/doi.org\/10.1109\/ACCESS.2022.3165574","journal-title":"IEEE Access."},{"key":"43339_CR8","doi-asserted-by":"crossref","unstructured":"Abhiram, A. P., Anzar, S. M. & Panthakkan, A. December. DeepSkinNet: a deep learning model for skin cancer detection. In: 2022 5th International Conference on Signal Processing and Information Security (ICSPIS) (97\u2013102). (IEEE, 2022).","DOI":"10.1109\/ICSPIS57063.2022.10002541"},{"key":"43339_CR9","doi-asserted-by":"publisher","first-page":"58848","DOI":"10.1109\/ACCESS.2025.3556479","volume":"13","author":"G. Ramella","year":"2025","unstructured":"Ramella, G. & Serino, L. CNN issues in skin lesion classification: data distribution and quantity. IEEE Access. 13, 58848\u201358862. (2025). https:\/\/doi.org\/10.1109\/ACCESS.2025.3556479","journal-title":"IEEE Access."},{"key":"43339_CR10","doi-asserted-by":"publisher","first-page":"5084","DOI":"10.1038\/s41598-025-89693-8","volume":"15","author":"N Veeramani","year":"2025","unstructured":"Veeramani, N. & Jayaraman, P. A promising AI based super resolution image reconstruction technique for early diagnosis of skin cancer. Sci. Rep. 15, 5084. https:\/\/doi.org\/10.1038\/s41598-025-89693-8 (2025).","journal-title":"Sci. Rep."},{"key":"43339_CR11","doi-asserted-by":"publisher","first-page":"33482","DOI":"10.1038\/s41598-025-15019-3","volume":"15","author":"M Kannan","year":"2025","unstructured":"Kannan, M. et al. An enhancement of machine learning model performance in disease prediction with synthetic data generation. Sci. Rep. 15, 33482. https:\/\/doi.org\/10.1038\/s41598-025-15019-3 (2025).","journal-title":"Sci. Rep."},{"key":"43339_CR12","doi-asserted-by":"publisher","first-page":"40204","DOI":"10.1038\/s41598-025-24081-w","volume":"15","author":"FH Najjar","year":"2025","unstructured":"Najjar, F. H. et al. Transformer-aided skin cancer classification using VGG19-based feature encoding. Sci. Rep. 15, 40204. https:\/\/doi.org\/10.1038\/s41598-025-24081-w (2025).","journal-title":"Sci. Rep."},{"key":"43339_CR13","doi-asserted-by":"publisher","first-page":"34776","DOI":"10.1038\/s41598-025-18570-1","volume":"15","author":"HT Halawani","year":"2025","unstructured":"Halawani, H. T. et al. Enhanced early skin cancer detection through fusion of vision transformer and CNN features using hybrid attention of EViT-Dens169. Sci. Rep. 15, 34776. https:\/\/doi.org\/10.1038\/s41598-025-18570-1 (2025).","journal-title":"Sci. Rep."},{"key":"43339_CR14","doi-asserted-by":"publisher","first-page":"1181","DOI":"10.1007\/s11760-022-02325-w","volume":"17","author":"I Bakkouri","year":"2023","unstructured":"Bakkouri, I. & Afdel, K. MLCA2F: Multi-level context attentional feature fusion for COVID-19 lesion segmentation from CT scans. SIViP 17, 1181\u20131188. https:\/\/doi.org\/10.1007\/s11760-022-02325-w (2023).","journal-title":"SIViP"},{"key":"43339_CR15","doi-asserted-by":"publisher","first-page":"10743","DOI":"10.1007\/s11042-022-12242-2","volume":"81","author":"I Bakkouri","year":"2022","unstructured":"Bakkouri, I. et al. BG-3DM2F: Bidirectional gated 3D multi-scale feature fusion for Alzheimer\u2019s disease diagnosis. Multimed Tools Appl. 81, 10743\u201310776. https:\/\/doi.org\/10.1007\/s11042-022-12242-2 (2022).","journal-title":"Multimed Tools Appl."},{"key":"43339_CR16","doi-asserted-by":"publisher","first-page":"20483","DOI":"10.1007\/s11042-019-07988-1","volume":"79","author":"I Bakkouri","year":"2020","unstructured":"Bakkouri, I. & Afdel, K. Computer-aided diagnosis (CAD) system based on multi-layer feature fusion network for skin lesion recognition in dermoscopy images. Multimed Tools Appl. 79, 20483\u201320518. https:\/\/doi.org\/10.1007\/s11042-019-07988-1 (2020).","journal-title":"Multimed Tools Appl."},{"key":"43339_CR17","doi-asserted-by":"publisher","first-page":"12939","DOI":"10.1007\/s11042-018-6267-z","volume":"78","author":"I Bakkouri","year":"2019","unstructured":"Bakkouri, I. & Afdel, K. Multi-scale CNN based on region proposals for efficient breast abnormality recognition. Multimed Tools Appl. 78, 12939\u201312960. https:\/\/doi.org\/10.1007\/s11042-018-6267-z (2019).","journal-title":"Multimed Tools Appl."},{"issue":"8","key":"43339_CR18","doi-asserted-by":"publisher","first-page":"4311","DOI":"10.1007\/s00521-023-09011-z","volume":"36","author":"AM Vidhyalakshmi","year":"2024","unstructured":"Vidhyalakshmi, A. M. & Kanchana, M. Classification of skin disease using a novel hybrid flash butterfly optimization from dermoscopic images. Neural Comput. Appl. 36 (8), 4311\u20134324 (2024).","journal-title":"Neural Comput. Appl."},{"issue":"5","key":"43339_CR19","doi-asserted-by":"publisher","first-page":"1795","DOI":"10.31557\/APJCP.2024.25.5.1795","volume":"25","author":"K. Kanchana","year":"2024","unstructured":"Kanchana, K., Kavitha, S., Anoop, K. J. & Chinthamani, B. Enhancing skin cancer classification using efficient net b0-b7 through convolutional neural networks and transfer learning with patient-specific data. Asian Pac. J. Cancer Prevention: APJCP. 25 (5), 1795 (2024).","journal-title":"Asian Pac. J. Cancer Prevention: APJCP"},{"key":"43339_CR20","doi-asserted-by":"crossref","unstructured":"Chatterjee, S., Gil, J. M. & Byun, Y. C. Early detection of multiclass skin lesions using transfer learning-based IncepX-ensemble model. IEEE Access (2024).","DOI":"10.1109\/ACCESS.2024.3432904"},{"key":"43339_CR21","doi-asserted-by":"crossref","unstructured":"Reka, S. S., Karthikeyan, H. L., Shakil, A. J., Venugopal, P. & Muniraj, M. Exploring quantum machine learning for enhanced skin lesion classification: A comparative study of implementation methods. IEEE Access (2024).","DOI":"10.1109\/ACCESS.2024.3434681"},{"key":"43339_CR22","doi-asserted-by":"crossref","unstructured":"Ji, Z. et al. Efam-net: A multi-class skin lesion classification model utilizing enhanced feature fusion and attention mechanisms. IEEE Access (2024).","DOI":"10.1109\/ACCESS.2024.3468612"},{"key":"43339_CR23","doi-asserted-by":"publisher","first-page":"67880","DOI":"10.1109\/ACCESS.2024.3397197","volume":"12","author":"W Di","year":"2024","unstructured":"Di, W. et al. ECRNet: Hybrid network for skin cancer identification. IEEE Access. 12, 67880\u201367888 (2024).","journal-title":"IEEE Access."},{"key":"43339_CR24","doi-asserted-by":"crossref","unstructured":"Ahmed, I. et al. Multi-model attentional fusion ensemble for accurate skin cancer classification. IEEE Access (2024).","DOI":"10.1109\/ACCESS.2024.3510224"},{"key":"43339_CR25","doi-asserted-by":"crossref","unstructured":"Saeed, A., Shehzad, K., Ahmed, S. & Azar, A. T. EG-VAN: a global and local attention based dual-branch ensemble network with advanced color balancing for multi-class skin cancer recognition. IEEE Access. (2025).","DOI":"10.1109\/ACCESS.2025.3561240"},{"key":"43339_CR26","doi-asserted-by":"publisher","first-page":"50738","DOI":"10.1109\/ACCESS.2024.3385340","volume":"12","author":"S Remya","year":"2024","unstructured":"Remya, S., Anjali, T. & Sugumaran, V. A novel transfer learning framework for multimodal skin lesion analysis. IEEE Access. 12, 50738\u201350754 (2024).","journal-title":"IEEE Access."},{"key":"43339_CR27","doi-asserted-by":"crossref","unstructured":"Shinde, R. et al. Squeeze-MNet: Precise skin cancer detection model for low computing IoT devices using transfer learning. Cancers 2023, 14, 12. (2022).","DOI":"10.3390\/cancers15010012"},{"issue":"1","key":"43339_CR28","doi-asserted-by":"publisher","first-page":"6","DOI":"10.1186\/s43046-024-00210-w","volume":"36","author":"D. Moturi","year":"2024","unstructured":"Moturi, D., Surapaneni, R. K. & Avanigadda, V. S. G. Developing an efficient method for melanoma detection using CNN techniques. J. Egypt. Natl Cancer Inst. 36 (1), 6 (2024).","journal-title":"J. Egypt. Natl Cancer Inst."},{"key":"43339_CR29","doi-asserted-by":"crossref","unstructured":"Alam, T. M. et al. An Efficient Deep Learning-Based Skin Cancer Classifier for an Imbalanced Dataset. Diagnostics 2022, 12, 2115. (2022).","DOI":"10.3390\/diagnostics12092115"},{"key":"43339_CR30","doi-asserted-by":"publisher","first-page":"16498","DOI":"10.1109\/ACCESS.2024.3360215","volume":"12","author":"Q. Su","year":"2024","unstructured":"Su, Q., Hamed, H. N. A., Isa, M. A., Hao, X. & Dai, X. A GAN-based data augmentation method for imbalanced multi-class skin lesion classification. IEEE Access. 12, 16498\u201316513. (2024). https:\/\/doi.org\/10.1109\/ACCESS.2024.3360215","journal-title":"IEEE Access."},{"key":"43339_CR31","doi-asserted-by":"publisher","first-page":"177019","DOI":"10.1109\/ACCESS.2025.3621107","volume":"13","author":"M. Kumar Kar","year":"2025","unstructured":"Kumar Kar, M., Venugopal, V. & Anoop, B. N. LeSegGAN: a hybrid attention-based gan for accurate lesion segmentation in dermatological images. IEEE Access. 13, 177019\u2013177035. (2025). https:\/\/doi.org\/10.1109\/ACCESS.2025.3621107","journal-title":"IEEE Access."},{"key":"43339_CR32","doi-asserted-by":"publisher","first-page":"122702","DOI":"10.1109\/ACCESS.2024.3417176","volume":"12","author":"A. Ravikumar","year":"2024","unstructured":"Ravikumar, A., Sriraman, H. & Chadha, C. Kumar Chattu, V. Alleviation of health data poverty for skin lesions using ACGAN: systematic review. IEEE Access. 12, 122702\u2013122723. (2024). https:\/\/doi.org\/10.1109\/ACCESS.2024.3417176","journal-title":"IEEE Access."},{"key":"43339_CR33","doi-asserted-by":"publisher","first-page":"166326","DOI":"10.1109\/ACCESS.2025.3612477","volume":"13","author":"R. H. Junejo","year":"2025","unstructured":"Junejo, R. H., Abbas, Q., Awais, M., Akram, T. & Aldajani, M. B. Federated ViT: a distributed deep learning framework for skin cancer classification. IEEE Access. 13, 166326\u2013166342. (2025). https:\/\/doi.org\/10.1109\/ACCESS.2025.3612477","journal-title":"IEEE Access."},{"key":"43339_CR34","doi-asserted-by":"publisher","first-page":"197312","DOI":"10.1109\/ACCESS.2025.3627877","volume":"13","author":"C. V. Aravinda","year":"2025","unstructured":"Aravinda, C. V., Raja Joseph, E. & Alasmari, S. A neural architecture search-driven quantum patch attention framework for skin disease recognition and classification with XAI vision transformers. IEEE Access. 13, 197312\u2013197328. (2025). https:\/\/doi.org\/10.1109\/ACCESS.2025.3627877","journal-title":"IEEE Access."},{"key":"43339_CR35","doi-asserted-by":"publisher","first-page":"101262","DOI":"10.1109\/ACCESS.2025.3578295","volume":"13","author":"W. Peng","year":"2025","unstructured":"Peng, W. et al. DBTU-Net: A dual branch network fusing transformer and U-net for skin lesion segmentation. IEEE Access. 13, 101262\u2013101273. (2025). https:\/\/doi.org\/10.1109\/ACCESS.2025.3578295","journal-title":"IEEE Access."},{"key":"43339_CR36","doi-asserted-by":"publisher","first-page":"32819","DOI":"10.1109\/ACCESS.2024.3370838","volume":"12","author":"D. Kundu","year":"2024","unstructured":"Kundu, D. et al. Federated deep learning for monkeypox disease detection on GAN-augmented dataset. IEEE Access. 12, 32819\u201332829. (2024). https:\/\/doi.org\/10.1109\/ACCESS.2024.3370838","journal-title":"IEEE Access."},{"key":"43339_CR37","doi-asserted-by":"publisher","first-page":"195673","DOI":"10.1109\/ACCESS.2024.3442827","volume":"13","author":"S. T. Tabibi","year":"2025","unstructured":"Tabibi, S. T., Nikravanshalmani, A. & Saboohi, H. An ensemble classifier based on diverse convolutional neural networks for skin lesions classification. IEEE Access. 13, 195673\u2013195686. (2025). https:\/\/doi.org\/10.1109\/ACCESS.2024.3442827","journal-title":"IEEE Access."},{"key":"43339_CR38","doi-asserted-by":"crossref","unstructured":"Noman, A., Beiji, Z., Zhu, C., Alhabib, M. & Alasri, A. Cascading size-dependent deep propagation (CADP): Addressing over-smoothing in graph few-shot dermatology classification. Neural Networks, p.108154. (2025).","DOI":"10.1016\/j.neunet.2025.108154"},{"key":"43339_CR39","doi-asserted-by":"crossref","unstructured":"Noman, A., Beiji, Z., Zhu, C., Alhabib, M. & Alasri, A. SEN-GAT: Synergistic Edge-Node graph attention networks for few-shot skin disease diagnosis. Digital Signal. Processing, p.105497. (2025).","DOI":"10.1016\/j.dsp.2025.105497"},{"issue":"1","key":"43339_CR40","doi-asserted-by":"publisher","first-page":"26409","DOI":"10.1038\/s41598-025-08552-8","volume":"15","author":"Y Karuna","year":"2025","unstructured":"Karuna, Y., Syamala, N., Ravikumar, C. V., Thakur, P. & Saritha Saladi. Modified energy-based GAN for intensity in homogeneity correction in brain MR images. Sci. Rep. 15 (1), 26409 (2025).","journal-title":"Sci. Rep."},{"issue":"2","key":"43339_CR41","doi-asserted-by":"publisher","first-page":"438","DOI":"10.3390\/electronics12020438","volume":"12","author":"M. Wei","year":"2023","unstructured":"Wei, M. et al. A skin disease classification model based on densenet and convnext fusion. Electronics 12 (2), 438 (2023).","journal-title":"Electronics"}],"container-title":["Scientific Reports"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-43339-5","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-43339-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-43339-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,21]],"date-time":"2026-04-21T23:47:21Z","timestamp":1776815241000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-43339-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,11]]},"references-count":41,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["43339"],"URL":"https:\/\/doi.org\/10.1038\/s41598-026-43339-5","relation":{},"ISSN":["2045-2322"],"issn-type":[{"value":"2045-2322","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,3,11]]},"assertion":[{"value":"28 December 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 March 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 March 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"13114"}}