{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T06:43:01Z","timestamp":1774939381606,"version":"3.50.1"},"reference-count":29,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2026,2,1]],"date-time":"2026-02-01T00:00:00Z","timestamp":1769904000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,2,1]],"date-time":"2026-02-01T00:00:00Z","timestamp":1769904000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Appl Intell"],"published-print":{"date-parts":[[2026,2]]},"DOI":"10.1007\/s10489-026-07111-6","type":"journal-article","created":{"date-parts":[[2026,2,25]],"date-time":"2026-02-25T05:15:19Z","timestamp":1771996519000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["CCNN-SCD: a deep composite architecture for skin cancer detection using feature engineering-aided convolutional neural networks for dermatological diagnosis"],"prefix":"10.1007","volume":"56","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2620-5074","authenticated-orcid":false,"given":"Madiha","family":"Hameed","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Aneela Zameer","family":"Jaffery","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Muhammad Yousaf","family":"Hamza","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9953-822X","authenticated-orcid":false,"given":"Muhammad Asif Zahoor","family":"Raja","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,2,25]]},"reference":[{"key":"7111_CR1","doi-asserted-by":"publisher","unstructured":"Abdar M et al (2021) Uncertainty quantification in skin cancer classification using three-way decision-based Bayesian deep learning.\u00a0Comput Biol\u00a0135(January):104418. Available at: https:\/\/doi.org\/10.1016\/j.compbiomed.2021.104418","DOI":"10.1016\/j.compbiomed.2021.104418"},{"key":"7111_CR2","doi-asserted-by":"publisher","unstructured":"Ain QU et al (2020) A genetic programming approach to feature construction for ensemble learning in skin cancer detection. In:\u00a0GECCO 2020 - Proceedings of the 2020 Genetic and Evolutionary Computation Conference. Association for Computing Machinery, pp 1186\u20131194. Available at: https:\/\/doi.org\/10.1145\/3377930.3390228","DOI":"10.1145\/3377930.3390228"},{"key":"7111_CR3","doi-asserted-by":"publisher","unstructured":"Akter MS et al (2022) Multi-class skin cancer classification architecture based on deep convolutional neural network. In:\u00a0Proceedings \u2013\u20092022 IEEE International Conference on Big Data, Big Data 2022. Institute of Electrical and Electronics Engineers Inc, pp 5404\u20135413. Available at: https:\/\/doi.org\/10.1109\/BigData55660.2022.10020302","DOI":"10.1109\/BigData55660.2022.10020302"},{"issue":"3","key":"7111_CR4","doi-asserted-by":"publisher","first-page":"97","DOI":"10.3390\/bioengineering9030097","volume":"9","author":"S Bechelli","year":"2022","unstructured":"Bechelli S, Delhommelle J (2022) Machine learning and deep learning algorithms for skin cancer classification from dermoscopic images. Bioengineering 9(3):97. https:\/\/doi.org\/10.3390\/bioengineering9030097","journal-title":"Bioengineering"},{"key":"7111_CR5","doi-asserted-by":"publisher","unstructured":"Haggenmu S et al (2021) Sciencedirect skin cancer classification via convolutional neural networks: systematic review of studies involving human experts, p 156. Available at: https:\/\/doi.org\/10.1016\/j.ejca.2021.06.049","DOI":"10.1016\/j.ejca.2021.06.049"},{"key":"7111_CR6","doi-asserted-by":"publisher","unstructured":"Haider KMM et al (2022) An enhanced CNN model for classifying skin cancer. In:\u00a0ACM International Conference Proceeding Series. Association for Computing Machinery, pp 456\u2013459. Available at: https:\/\/doi.org\/10.1145\/3542954.3543019","DOI":"10.1145\/3542954.3543019"},{"key":"7111_CR7","doi-asserted-by":"publisher","unstructured":"Hameed M, Zameer A, Asif M et al (2024) A comprehensive systematic review: advancements in skin cancer classification and segmentation using the ISIC dataset. Available at: https:\/\/doi.org\/10.32604\/cmes.2024.050124","DOI":"10.32604\/cmes.2024.050124"},{"key":"7111_CR8","doi-asserted-by":"publisher","DOI":"10.1140\/epjp\/s13360-024-05220-0","author":"M Hameed","year":"2024","unstructured":"Hameed M, Zameer A, Khan SH (2024) ARiViT: attention-based residual-integrated vision transformer for noisy brain medical image classification. The European Physical Journal Plus. https:\/\/doi.org\/10.1140\/epjp\/s13360-024-05220-0","journal-title":"The European Physical Journal Plus"},{"issue":"1","key":"7111_CR9","doi-asserted-by":"publisher","first-page":"32798","DOI":"10.1038\/s41598-025-17891-5","volume":"15","author":"M Hameed","year":"2025","unstructured":"Hameed M et al (2025) Acute myeloid leukemia classification using ReLViT and detection with YOLO enhanced by adversarial networks on bone marrow images. Sci Rep 15(1):32798. https:\/\/doi.org\/10.1038\/s41598-025-17891-5","journal-title":"Sci Rep"},{"key":"7111_CR10","doi-asserted-by":"publisher","unstructured":"Hasan K et al (2022) Informatics in medicine unlocked DermoExpert: skin lesion classification using a hybrid convolutional neural network through segmentation, transfer learning, and augmentation.\u00a0Infor Med Unlocked\u00a028:100819. Available at: https:\/\/doi.org\/10.1016\/j.imu.2021.100819","DOI":"10.1016\/j.imu.2021.100819"},{"key":"7111_CR11","doi-asserted-by":"publisher","unstructured":"Ho J et al (2021) ScienceDirect Combining CNN-based histologic whole slide image analysis and patient data to improve skin cancer classification, vol 149, pp 94\u2013101. Available at: https:\/\/doi.org\/10.1016\/j.ejca.2021.02.032","DOI":"10.1016\/j.ejca.2021.02.032"},{"key":"7111_CR12","doi-asserted-by":"publisher","unstructured":"Ibraheem MR, Elmogy M (2020) A non-invasive automatic skin cancer detection system for characterizing malignant melanoma from seborrheic keratosis. In:\u00a02020 2nd International Conference on Computer and Information Sciences, ICCIS 2020. Institute of Electrical and Electronics Engineers Inc. Available at: https:\/\/doi.org\/10.1109\/ICCIS49240.2020.9257712","DOI":"10.1109\/ICCIS49240.2020.9257712"},{"key":"7111_CR13","doi-asserted-by":"publisher","unstructured":"Khattar S, Kaur R, Gupta G (2023) A review on preprocessing, segmentation and classification techniques for detection of skin cancer.\u00a02nd Edition of IEEE Delhi Section Owned Conference, DELCON 2023 - Proceedings [Preprint]. Available at: https:\/\/doi.org\/10.1109\/DELCON57910.2023.10127546","DOI":"10.1109\/DELCON57910.2023.10127546"},{"issue":"December 2023","key":"7111_CR14","doi-asserted-by":"publisher","first-page":"p114451","DOI":"10.1016\/j.fct.2024.114451","volume":"185","author":"M Koushki","year":"2024","unstructured":"Koushki M et al (2024) Screening the critical protein subnetwork to delineate potential mechanisms and protective agents associated with arsenic-induced cutaneous squamous cell carcinoma: A toxicogenomic study. Food Chem Toxicol 185(December 2023):p114451. https:\/\/doi.org\/10.1016\/j.fct.2024.114451. Available at:","journal-title":"Food Chem Toxicol"},{"key":"7111_CR15","doi-asserted-by":"publisher","first-page":"108742","DOI":"10.1016\/j.compbiomed.2024.108742","volume":"178","author":"UA Lyakhova","year":"2024","unstructured":"Lyakhova UA, Lyakhov PA (2024) Systematic review of approaches to detection and classification of skin cancer using artificial intelligence\u202f: development and prospects. Comput Biol Med 178:108742. https:\/\/doi.org\/10.1016\/j.compbiomed.2024.108742","journal-title":"Comput Biol Med"},{"key":"7111_CR16","doi-asserted-by":"publisher","unstructured":"Mahbod A et al (2020) Computer methods and programs in biomedicine the effects of skin lesion segmentation on the performance of dermatoscopic image classification, p 197. Available at: https:\/\/doi.org\/10.1016\/j.cmpb.2020.105725","DOI":"10.1016\/j.cmpb.2020.105725"},{"key":"7111_CR17","doi-asserted-by":"publisher","unstructured":"Mirbeik A et al (2022) Real-time high-resolution millimeter-wave imaging for in-vivo skin cancer diagnosis.\u00a0Sci Rep\u00a012(1). Available at: https:\/\/doi.org\/10.1038\/s41598-022-09047-6","DOI":"10.1038\/s41598-022-09047-6"},{"key":"7111_CR18","doi-asserted-by":"publisher","DOI":"10.1016\/j.micpro.2022.104660","author":"E Mushtaq","year":"2022","unstructured":"Mushtaq E, Zameer A, Khan A (2022) A two-stage stacked ensemble intrusion detection system using five base classifiers and MLP with optimal feature selection. Microprocess Microsyst. https:\/\/doi.org\/10.1016\/j.micpro.2022.104660","journal-title":"Microprocess Microsyst"},{"key":"7111_CR19","doi-asserted-by":"publisher","first-page":"103997","DOI":"10.1016\/j.bspc.2022.103997","volume":"78","author":"K Nakai","year":"2022","unstructured":"Nakai K, Chen YW, Han XH (2022) Enhanced deep bottleneck transformer model for skin lesion classification. Biomed Signal Process Control 78:103997. https:\/\/doi.org\/10.1016\/j.bspc.2022.103997","journal-title":"Biomed Signal Process Control"},{"key":"7111_CR20","doi-asserted-by":"publisher","unstructured":"Qasim Gilani S et al (2023) Skin cancer classification using deep spiking neural network.\u00a0J Digit Imaging [Preprint]. Available at: https:\/\/doi.org\/10.1007\/s10278-023-00776-2","DOI":"10.1007\/s10278-023-00776-2"},{"key":"7111_CR21","doi-asserted-by":"publisher","unstructured":"Qureshi MA et al (2020) Common cancers in Karachi, Pakistan: 2010\u20132019 cancer data from the dow cancer registry.\u00a0Pak J Med Sci\u00a036(7):1572\u20131578. Available at: https:\/\/doi.org\/10.12669\/pjms.36.7.3056","DOI":"10.12669\/pjms.36.7.3056"},{"issue":"4","key":"7111_CR22","doi-asserted-by":"publisher","first-page":"339","DOI":"10.30699\/ijp.2017.27990","volume":"12","author":"M Ram","year":"2017","unstructured":"Ram M, Najafi A, Shakeri MT (2017) Classification and biomarker genes selection for cancer gene expression data using random forest. J Pathol Iran J Pathol 12(4):339\u2013347","journal-title":"J Pathol Iran J Pathol"},{"key":"7111_CR23","doi-asserted-by":"publisher","unstructured":"Ramkumar K et al (2024) Engineering applications of artificial intelligence a novel deep learning framework based swin transformer for dermal cancer cell classification.\u00a0Eng Appl Artif Intell 133(PB):108097. Available at: https:\/\/doi.org\/10.1016\/j.engappai.2024.108097","DOI":"10.1016\/j.engappai.2024.108097"},{"issue":"July","key":"7111_CR24","doi-asserted-by":"publisher","first-page":"112013","DOI":"10.1016\/j.asoc.2024.112013","volume":"164","author":"HC Reis","year":"2024","unstructured":"Reis HC, Turk V (2024) Fusion of transformer attention and CNN features for skin cancer detection. Appl Soft Comput 164(July):112013. https:\/\/doi.org\/10.1016\/j.asoc.2024.112013","journal-title":"Appl Soft Comput"},{"key":"7111_CR25","doi-asserted-by":"publisher","first-page":"8","DOI":"10.1016\/j.patrec.2020.05.019","volume":"136","author":"DDA Rodrigues","year":"2020","unstructured":"Rodrigues DDA et al (2020) A new approach for classification skin lesion based on transfer learning, deep learning, and IoT system. Pattern Recognit Lett 136:8\u201315. https:\/\/doi.org\/10.1016\/j.patrec.2020.05.019","journal-title":"Pattern Recognit Lett"},{"issue":"1","key":"7111_CR26","doi-asserted-by":"publisher","first-page":"7","DOI":"10.3322\/caac.21708","volume":"72","author":"RL Siegel","year":"2022","unstructured":"Siegel RL et al (2022) Cancer statistics, 2022. CA Cancer J Clin 72(1):7\u201333. https:\/\/doi.org\/10.3322\/caac.21708","journal-title":"CA Cancer J Clin"},{"issue":"1","key":"7111_CR27","doi-asserted-by":"publisher","first-page":"94","DOI":"10.1016\/j.bjoms.2022.11.280","volume":"61","author":"D Tighe","year":"2023","unstructured":"Tighe D et al (2023) Machine learning methods applied to audit of surgical margins after curative surgery for facial (non-melanoma) skin cancer. Br J Oral Maxillofac Surg 61(1):94\u2013100. https:\/\/doi.org\/10.1016\/j.bjoms.2022.11.280","journal-title":"Br J Oral Maxillofac Surg"},{"key":"7111_CR28","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0285410","author":"A Zameer","year":"2023","unstructured":"Zameer A (2023) Short-term solar energy forecasting: integrated computational intelligence of LSTMs and GRU. PLoS One. https:\/\/doi.org\/10.1371\/journal.pone.0285410","journal-title":"PLoS One"},{"key":"7111_CR29","doi-asserted-by":"publisher","unstructured":"Zhao G et al (2024) PMANet: Progressive multi-stage attention networks for skin disease classification.\u00a0Image Vision Comput\u00a0149(July):105166. Available at: https:\/\/doi.org\/10.1016\/j.imavis.2024.105166","DOI":"10.1016\/j.imavis.2024.105166"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-026-07111-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-026-07111-6","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-026-07111-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T05:13:58Z","timestamp":1774934038000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-026-07111-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,2]]},"references-count":29,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2026,2]]}},"alternative-id":["7111"],"URL":"https:\/\/doi.org\/10.1007\/s10489-026-07111-6","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"value":"0924-669X","type":"print"},{"value":"1573-7497","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,2]]},"assertion":[{"value":"30 December 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 January 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 February 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":"This manuscript does not involve any animal or human-based experiments.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical and informed consent"}},{"value":"The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"110"}}