{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,27]],"date-time":"2026-07-27T10:59:17Z","timestamp":1785149957511,"version":"3.55.0"},"reference-count":40,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2026,7,27]],"date-time":"2026-07-27T00:00:00Z","timestamp":1785110400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Background\/Objectives: Melanoma is a life-threatening skin cancer characterized by aggressive progression and high metastatic potential, making early diagnosis essential for improving patient survival and treatment outcomes. However, accurate automated skin lesion classification remains challenging due to variations in lesion appearance, illumination, image quality, and the presence of artifacts. This study proposes a unified framework for robust multi-class skin cancer classification by integrating preprocessing, lesion segmentation, feature extraction, optimization, and classification within a single end-to-end architecture. Methods: The proposed framework employs an iterative hair artifact removal strategy based on the fusion of Frangi vesselness filtering, Gabor texture filtering, morphological refinement, and Telea inpainting to preserve lesion integrity. A novel dermoscopic lesion segmentation network (CutisNet) is introduced to accurately delineate lesion boundaries. Hybrid representation learning combines deep features extracted using MobileNetV2 with uniquely selected handcrafted descriptors to capture complementary texture, structural, and contextual information. Gray Wolf Optimization is utilized for feature fusion and refinement, while a hybrid GNN\u2013CNN classifier performs robust multi-class skin lesion classification. Results: Extensive experiments conducted on multiple benchmark dermoscopic datasets demonstrate the effectiveness and generalization capability of the proposed framework. The proposed model consistently outperformed existing state-of-the-art methods, achieving a maximum classification accuracy of 96.7% on the PH2 dataset while maintaining competitive performance across other benchmark datasets. Conclusions: The proposed framework effectively integrates novel preprocessing, segmentation, hybrid feature representation, feature optimization, and classification strategies to improve the robustness and accuracy of automated skin cancer classification. These results demonstrate its potential to support reliable computer-aided diagnosis and assist clinicians in the early detection of skin cancer.<\/jats:p>","DOI":"10.3390\/diagnostics16152351","type":"journal-article","created":{"date-parts":[[2026,7,27]],"date-time":"2026-07-27T09:47:41Z","timestamp":1785145661000},"page":"2351","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Hybrid Vision Transformer\u2013CNN Architecture with Optimized Feature Selection for Skin Cancer Classification"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5709-2140","authenticated-orcid":false,"given":"Abrar","family":"Almjally","sequence":"first","affiliation":[{"name":"Information Technology Department, College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh\u00a013318, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Munazza","family":"Aziz","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering, Riphah International University, I-14, Islamabad 44000, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shaheryar","family":"Najam","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, Bahria University, H-11, Islamabad 44000, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4582-1606","authenticated-orcid":false,"given":"Alaa","family":"Menshawi","sequence":"additional","affiliation":[{"name":"Information Systems Department, College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 13318, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Meteb","family":"Altaf","sequence":"additional","affiliation":[{"name":"Disability Research Institute, Health Sector, King Abdulaziz City for Science and Technology Riyadh, Riyadh 11442, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2262-2340","authenticated-orcid":false,"given":"Abdullah Fawaz","family":"Aljulayfi","sequence":"additional","affiliation":[{"name":"Department of Software Engineering, College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Al-Kharj 16273, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ahmad","family":"Jalal","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Air University, E-9, Islamabad 44000, Pakistan"},{"name":"Department of Computer Science and Engineering, College of Informatics, Korea University, Seoul 02841, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,7,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"971","DOI":"10.1016\/S0140-6736(18)31559-9","article-title":"Melanoma","volume":"392","author":"Schadendorf","year":"2018","journal-title":"Lancet"},{"key":"ref_2","first-page":"472","article-title":"Melanoma staging: Evidence-based changes in the American Joint Committee on Cancer eighth edition cancer staging manual","volume":"67","author":"Gershenwald","year":"2017","journal-title":"CA Cancer J. Clin."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"2803","DOI":"10.1038\/s41467-022-30471-9","article-title":"Fast raster-scan optoacoustic mesoscopy enables assessment of human melanoma microvasculature in vivo","volume":"13","author":"He","year":"2022","journal-title":"Nat. Commun."},{"key":"ref_4","doi-asserted-by":"crossref","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":"ref_5","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1038\/nature21056","article-title":"Dermatologist-level classification of skin cancer with deep neural networks","volume":"542","author":"Esteva","year":"2017","journal-title":"Nature"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Codella, N.C.F., Gutman, D., Celebi, M.E., Helba, B., Marchetti, M.A., Dusza, S.W., Kalloo, A., Liopyris, K., Mishra, N., and Kittler, H. (2018, January 4\u20137). Skin lesion analysis toward melanoma detection: A challenge at the 2017 International Symposium on Biomedical Imaging (ISBI), hosted by the International Skin Imaging Collaboration (ISIC). Proceedings of the IEEE 15th International Symposium on Biomedical Imaging (ISBI), Washington, DC, USA.","DOI":"10.1109\/ISBI.2018.8363547"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Van Der Maaten, L., and Weinberger, K.Q. (2017, January 21\u201326). Densely connected convolutional networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1016\/j.cmpb.2018.05.027","article-title":"Skin lesion segmentation in dermoscopy images via deep full resolution convolutional networks","volume":"162","author":"Choi","year":"2018","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Li, Y., and Shen, L. (2018). Skin lesion analysis towards melanoma detection using deep learning network. Sensors, 18.","DOI":"10.3390\/s18020556"},{"key":"ref_10","unstructured":"Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., and Gelly, S. (2020). An image is worth 16 \u00d7 16 words: Transformers for image recognition at scale. arXiv."},{"key":"ref_11","doi-asserted-by":"crossref","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":"ref_12","unstructured":"Combalia, M., Codella, N.C.F., Rotemberg, V., Helba, B., Vilaplana, V., Reiter, O., Carrera, C., Barreiro, A., Halpern, A.C., and Puig, S. (2019). BCN20000: Dermoscopic lesions in the wild. arXiv."},{"key":"ref_13","unstructured":"Samek, W., Wiegand, T., and M\u00fcller, K.R. (2017). Explainable artificial intelligence: Understanding, visualizing and interpreting deep learning models. arXiv."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"180161","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":"ref_15","doi-asserted-by":"crossref","unstructured":"Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., and Chen, L.C. (2018, January 18\u201323). MobileNetV2: Inverted residuals and linear bottlenecks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00474"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Ouyang, Z., Zhao, S., Cheng, Z., Duan, Y., Chen, Z., Zhang, N., Liang, D., and Hu, Z. (2022). Dynamic PET imaging using dual texture features. Front. Comput. Neurosci., 15.","DOI":"10.3389\/fncom.2021.819840"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"106221","DOI":"10.1016\/j.dib.2020.106221","article-title":"PAD-UFES-20: A skin lesion dataset composed of patient data and clinical images collected from smartphones","volume":"32","author":"Pacheco","year":"2020","journal-title":"Data Brief."},{"key":"ref_18","doi-asserted-by":"crossref","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":"2019","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Mendon\u00e7a, T., Ferreira, P.M., Marques, J.S., Mar\u00e7al, A.R.S., and Rozeira, J. (2013, January 3\u20137). PH2\u2014A dermoscopic image database for research and benchmarking. Proceedings of the 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Osaka, Japan.","DOI":"10.1109\/EMBC.2013.6610779"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"37775","DOI":"10.1038\/s41598-025-14839-7","article-title":"Multimodal dual-stage feature refinement for robust skin lesion classification","volume":"15","author":"Khurshid","year":"2025","journal-title":"Sci. Rep."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Pham, N.L.T., Pham, D.D., Le, T.D., and Huynh, K.T. (2025). A multimodal deep ensemble framework for skin lesion classification. Proceedings of the International Symposium on Integrated Uncertainty in Knowledge Modelling and Decision Making, Springer.","DOI":"10.1007\/978-981-96-4606-7_9"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Khurshid, M., Vatsa, M., and Singh, R. (2024, January 27\u201330). Optimizing skin lesion classification via multimodal data and auxiliary task integration. Proceedings of the IEEE International Symposium on Biomedical Imaging (ISBI), Athens, Greece.","DOI":"10.1109\/ISBI56570.2024.10635277"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"110604","DOI":"10.1016\/j.patcog.2024.110604","article-title":"Joint-individual fusion structure with fusion attention module for multi-modal skin cancer classification","volume":"154","author":"Tang","year":"2024","journal-title":"Pattern Recognit."},{"key":"ref_24","unstructured":"Uliana, J.J.M., and Krohling, R.A. (2025). Diffusion models applied to skin and oral cancer classification. arXiv."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"E1005","DOI":"10.32598\/JTRM.1.1005","article-title":"Advanced melanoma detection using the ConvMixer model on ISIC and PH2 dermatoscopic images","volume":"1","author":"Moodi","year":"2025","journal-title":"J. Transl. Regen. Med."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"506","DOI":"10.7150\/jca.101574","article-title":"Hybrid model with wavelet decomposition and EfficientNet for accurate skin cancer classification","volume":"16","author":"Aboulmira","year":"2025","journal-title":"J. Cancer"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Gamil, S., Zeng, F., Alrifaey, M., Asim, M., and Ahmad, N. (2024). An efficient AdaBoost algorithm for enhancing skin cancer detection and classification. Algorithms, 17.","DOI":"10.3390\/a17080353"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"103692","DOI":"10.1016\/j.rineng.2024.103692","article-title":"An innovative deep learning framework for skin cancer detection employing ConvNeXtV2 and focal self-attention mechanisms","volume":"25","author":"Pacal","year":"2025","journal-title":"Results Eng."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Yang, J., Qin, H., Wang, J., Yee, L., Prajapat, S., Kumar, G., Balusamy, B., Bashir, A.K., and Omar, M. (2025). IoT-driven skin cancer detection: Active learning and hyperparameter optimization for enhanced accuracy. IEEE J. Biomed. Health Inf., 1\u201311.","DOI":"10.1109\/JBHI.2025.3578419"},{"key":"ref_30","doi-asserted-by":"crossref","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":"ref_31","doi-asserted-by":"crossref","unstructured":"Xu, J., Gao, Y., Liu, W., Huang, K., Zhao, S., Lu, L., Wang, X., Hua, X.S., Wang, Y., and Chen, X. (2022). RemixFormer: A transformer model for precision skin tumor differential diagnosis via multi-modal imaging and non-imaging data. Medical Image Computing and Computer-Assisted Intervention (MICCAI), Springer.","DOI":"10.1007\/978-3-031-16437-8_60"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"107502","DOI":"10.1016\/j.patcog.2020.107502","article-title":"Multi-label classification of multi-modality skin lesion via hyper-connected convolutional neural network","volume":"107","author":"Bi","year":"2020","journal-title":"Pattern Recognit."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"102307","DOI":"10.1016\/j.media.2021.102307","article-title":"FusionM4Net: A multi-stage multi-modal learning algorithm for multi-label skin lesion classification","volume":"76","author":"Tang","year":"2022","journal-title":"Med. Image Anal."},{"key":"ref_34","unstructured":"Agarwal, S., and Mahto, A.K. (2025). Skin cancer classification: Hybrid CNN-transformer models with KAN-based fusion. arXiv."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1723","DOI":"10.1007\/s10278-023-00837-6","article-title":"An integrated ensemble network model for skin abnormality detection with combined textural features","volume":"36","author":"Sharafudeen","year":"2023","journal-title":"J. Digit. Imaging"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Joshi, P., Ruangrit, C., and Konghuayrob, P. (2025, January 15\u201318). Mitigating racial bias in skin lesion classification with a novel deep learning-driven dataset. Proceedings of the 17th Biomedical Engineering International Conference (BMEiCON), Chiang Mai, Thailand.","DOI":"10.1109\/BMEiCON66226.2025.11113692"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Gunaratne, R.D., and Lakmal, H.K.I.S. (2025, January 19\u201321). Seeing beyond the surface: A transformer-based approach to early skin cancer detection. Proceedings of the 9th SLAAI International Conference on Artificial Intelligence (SLAAI-ICAI), Colombo, Sri Lanka.","DOI":"10.1109\/SLAAI-ICAI68534.2025.11318497"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Srivastava, R., Ong, E.P., Lee, J.W.E., Tan, N.C., Oh, D.A.Q., and Oh, C.C. (2025, January 14\u201318). Exploring the effect of race in automated skin cancer detection. Proceedings of the 47th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Copenhagen, Denmark.","DOI":"10.1109\/EMBC58623.2025.11253322"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"339","DOI":"10.1002\/jemt.23908","article-title":"Skin cancer detection from dermoscopic images using deep learning and fuzzy k-means clustering","volume":"85","author":"Nawaz","year":"2022","journal-title":"Microsc. Res. Tech."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"e23180","DOI":"10.1002\/ima.23180","article-title":"A hybrid convolutional neural network model for the classification of multi-class skin cancer","volume":"34","author":"Toprak","year":"2024","journal-title":"Int. J. Imaging Syst. Technol."}],"container-title":["Diagnostics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2075-4418\/16\/15\/2351\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,27]],"date-time":"2026-07-27T10:02:15Z","timestamp":1785146535000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2075-4418\/16\/15\/2351"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7,27]]},"references-count":40,"journal-issue":{"issue":"15","published-online":{"date-parts":[[2026,8]]}},"alternative-id":["diagnostics16152351"],"URL":"https:\/\/doi.org\/10.3390\/diagnostics16152351","relation":{},"ISSN":["2075-4418"],"issn-type":[{"value":"2075-4418","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,7,27]]}}}