{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,7]],"date-time":"2026-07-07T13:13:45Z","timestamp":1783430025031,"version":"3.54.6"},"reference-count":48,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2025,2,8]],"date-time":"2025-02-08T00:00:00Z","timestamp":1738972800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>Skin cancer poses a significant global health concern, demanding early diagnosis to enhance patient outcomes and alleviate healthcare burdens. Despite advancements in automated diagnosis systems, most existing approaches primarily address binary classification, with limited focus on distinguishing among multiple skin cancer classes. Multiclass classification poses significant challenges due to intra-class variations and inter-class similarities, often leading to misclassification. These issues stem from subtle differences between skin cancer types and shared features across various classes. This paper proposes an attention-based Inception-Residual CNN (AIR-CNN) model specially designed to tackle the challenges related to multiclass skin cancer classification. Incorporating the attention mechanism model effectively focuses on the most relevant features, enhancing its ability to distinguish between visually similar classes and those with intra-class variations. The attention mechanism also facilitates effective training with limited samples. The inclusion of Inception-Residual (IR) blocks mitigates vanishing gradients, improves multi-scale feature extraction, and reduces parameters, creating a lightweight yet accurate model. The experimental evaluation of the ISIC 2019 dataset demonstrates superior performance with 91.63% accuracy and fewer parameters than state-of-the-art methods, which makes it suitable for practical applications, thus contributing to the advancement of automated skin cancer diagnosis systems.<\/jats:p>","DOI":"10.3390\/info16020120","type":"journal-article","created":{"date-parts":[[2025,2,10]],"date-time":"2025-02-10T03:39:47Z","timestamp":1739158787000},"page":"120","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Attention-Based Inception-Residual CNN: Skin Cancer Diagnosis with Attention-Based Inception-Residual CNN Model"],"prefix":"10.3390","volume":"16","author":[{"given":"Sara","family":"Younas","sequence":"first","affiliation":[{"name":"Department of Computer Science, COMSATS University Islamabad, Lahore Campus, Lahore 54000, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5040-7656","authenticated-orcid":false,"given":"Allah Bux","family":"Sargano","sequence":"additional","affiliation":[{"name":"Department of Computer Science, COMSATS University Islamabad, Lahore Campus, Lahore 54000, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lihua","family":"You","sequence":"additional","affiliation":[{"name":"National Centre for Computer Animation, Bournemouth University, Bournemouth BH12 5BB, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9758-9162","authenticated-orcid":false,"given":"Zulfiqar","family":"Habib","sequence":"additional","affiliation":[{"name":"Department of Computer Science, COMSATS University Islamabad, Lahore Campus, Lahore 54000, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,2,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"811","DOI":"10.1007\/s10462-020-09865-y","article-title":"Deep learning techniques for skin lesion analysis and melanoma cancer detection: A survey of state-of-the-art","volume":"54","author":"Adegun","year":"2021","journal-title":"Artif. Intell. Rev."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"26255","DOI":"10.1007\/s11042-021-10952-7","article-title":"Skin cancer disease images classification using deep learning solutions","volume":"80","author":"Mijwil","year":"2021","journal-title":"Multimed. Tools Appl."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"5:1","DOI":"10.1147\/JRD.2017.2708299","article-title":"Deep Learning Ensembles for Melanoma Recognition in Dermoscopy Images","volume":"61","author":"Codella","year":"2016","journal-title":"IBM J. Res. Dev."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"209","DOI":"10.3322\/caac.21660","article-title":"Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries","volume":"71","author":"Sung","year":"2021","journal-title":"CA Cancer J. Clin."},{"key":"ref_5","unstructured":"(2019). Joinpoint Trends in Cancer Incidence Rates for Selected Sites in Two Age Groups, US, 1995\u20132015 35 Figure S6. Trends in Cancer Death Rates for Selected Sites, American Cancer Society."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Tan, T.Y., Zhang, L., and Jiang, M. (2016, January 13\u201315). An intelligent decision support system for skin cancer detection from dermoscopic images. Proceedings of the 2016 12th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD), Changsha, China.","DOI":"10.1109\/FSKD.2016.7603521"},{"key":"ref_7","first-page":"780","article-title":"Melanoma Skin Cancer Detection using Image Processing and Machine Learning","volume":"3","author":"Vijayalakshmi","year":"2019","journal-title":"Int. J. Trend Sci. Res. Dev."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"101","DOI":"10.46501\/IJMTST061118","article-title":"Classification of Skin cancer using deep learning, Convolutional Neural Networks\u2014Opportunities and vulnerabilities- A systematic Review","volume":"6","author":"Manne","year":"2020","journal-title":"Int. J. Mod. Trends Sci. Technol."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1325","DOI":"10.1007\/s10278-020-00371-9","article-title":"Classification of Skin Lesions into Seven Classes Using Transfer Learning with AlexNet","volume":"33","author":"Hosny","year":"2020","journal-title":"J. Digit. Imaging"},{"key":"ref_10","doi-asserted-by":"crossref","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":"ref_11","doi-asserted-by":"crossref","unstructured":"Waheed, Z., Waheed, A., Zafar, M., and Riaz, F. (2017, January 8\u20139). An efficient machine learning approach for the detection of melanoma using dermoscopic images. Proceedings of the 2017 International Conference on Communication, Computing and Digital Systems (C-CODE), Islamabad, Pakistan.","DOI":"10.1109\/C-CODE.2017.7918949"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"147858","DOI":"10.1109\/ACCESS.2020.3014701","article-title":"Region-of-Interest Based Transfer Learning Assisted Framework for Skin Cancer Detection","volume":"8","author":"Ashraf","year":"2020","journal-title":"IEEE Access"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"101843","DOI":"10.1016\/j.compmedimag.2020.101843","article-title":"Automated multi-class classification of skin lesions through deep convolutional neural network with dermoscopic images","volume":"88","author":"Iqbal","year":"2021","journal-title":"Comput. Med. Imaging Graph."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"191","DOI":"10.1016\/j.ejca.2021.06.047","article-title":"A benchmark for neural network robustness in skin cancer classification","volume":"155","author":"Maron","year":"2021","journal-title":"Eur. J. Cancer"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"100659","DOI":"10.1016\/j.imu.2021.100659","article-title":"An approach for multiclass skin lesion classification based on ensemble learning","volume":"25","author":"Rahman","year":"2021","journal-title":"Inform. Med. Unlocked"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.pnmrs.2018.06.001","article-title":"Pros and cons of ultra-high-field MRI\/MRS for human application","volume":"109","author":"Ladd","year":"2018","journal-title":"Prog. Nucl. Magn. Reson. Spectrosc."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Goyal, M., Knackstedt, T., Yan, S., and Hassanpour, S. (2020). Artificial intelligence-based image classification methods for diagnosis of skin cancer: Challenges and opportunities. Comput. Biol. Med., 127.","DOI":"10.1016\/j.compbiomed.2020.104065"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"168239","DOI":"10.1016\/j.ijleo.2021.168239","article-title":"Classification of human skin cancer using Stokes-Mueller decomposition method and artificial intelligence models","volume":"249","author":"Luu","year":"2022","journal-title":"Optik"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"351","DOI":"10.1007\/s11036-020-01672-7","article-title":"A Review of Deep Learning on Medical Image Analysis","volume":"26","author":"Wang","year":"2021","journal-title":"Mob. Netw. Appl."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Alom, M.Z., Aspiras, T., Taha, T.M., and Asari, V.K. (2019). Skin Cancer Segmentation and Classification with NABLA-N and Inception Recurrent Residual Convolutional Networks. arXiv.","DOI":"10.1109\/NAECON.2018.8556737"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"283","DOI":"10.1007\/s10278-018-0079-6","article-title":"Hello World Deep Learning in Medical Imaging","volume":"31","author":"Lakhani","year":"2018","journal-title":"J. Digit. Imaging"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1096","DOI":"10.1109\/JBHI.2018.2845939","article-title":"A Survey of Feature Extraction in Dermoscopy Image Analysis of Skin Cancer","volume":"23","author":"Barata","year":"2019","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"112961","DOI":"10.1016\/j.eswa.2019.112961","article-title":"Multi-class multi-level classification algorithm for skin lesions classification using machine learning techniques","volume":"141","author":"Hameed","year":"2020","journal-title":"Expert Syst. Appl."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"3073","DOI":"10.1007\/s11063-020-10364-y","article-title":"A Convolutional Neural Network Framework for Accurate Skin Cancer Detection","volume":"53","year":"2021","journal-title":"Neural Process. Lett."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"7989","DOI":"10.1007\/s00521-022-06922-1","article-title":"A comparative study of fourteen deep learning networks for multi skin lesion classification (MSLC) on unbalanced data","volume":"35","author":"Arora","year":"2022","journal-title":"Neural Comput. Appl."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"380","DOI":"10.1007\/s00530-024-01590-8","article-title":"Hierarchical and progressive learning with key point sensitive loss for sonar image classification","volume":"30","author":"Chen","year":"2024","journal-title":"Multimed. Syst."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"2800","DOI":"10.1080\/02664763.2018.1441383","article-title":"The relative performance of ensemble methods with deep convolutional neural networks for image classification","volume":"45","author":"Ju","year":"2018","journal-title":"J. Appl. Stat."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"28477","DOI":"10.1007\/s11042-020-09388-2","article-title":"A multi-class skin Cancer classification using deep convolutional neural networks","volume":"79","author":"Chaturvedi","year":"2020","journal-title":"Multimed. Tools Appl."},{"key":"ref_29","doi-asserted-by":"crossref","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":"ref_30","doi-asserted-by":"crossref","unstructured":"Raza, R., Zulfiqar, F., Tariq, S., Anwar, G.B., Sargano, A.B., and Habib, Z. (2022). Melanoma classification from dermoscopy images using ensemble of convolutional neural networks. Mathematics, 10.","DOI":"10.3390\/math10010026"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Kaur, R., Gholamhosseini, H., Sinha, R., and Lind\u00e9n, M. (2022). Melanoma Classification Using a Novel Deep Convolutional Neural Network with Dermoscopic Images. Sensors, 22.","DOI":"10.1186\/s12880-022-00829-y"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Alzubaidi, L., Al-Amidie, M., Al-Asadi, A., Humaidi, A.J., Al-Shamma, O., Fadhel, M.A., Zhang, J., Santamar\u00eda, J., and Duan, Y. (2021). Novel transfer learning approach for medical imaging with limited labeled data. Cancers, 13.","DOI":"10.3390\/cancers13071590"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Datta, S.K., Shaikh, M.A., Srihari, S.N., and Gao, M. (2021). Soft Attention Improves Skin Cancer Classification Performance. Interpretability of Machine Intelligence in Medical Image Computing, and Topological Data Analysis and Its Applications for Medical Data, Springer.","DOI":"10.1007\/978-3-030-87444-5_2"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Hsu, B.W.Y., and Tseng, V.S. (2022). Hierarchy-aware contrastive learning with late fusion for skin lesion classification. Comput. Methods Programs Biomed., 216.","DOI":"10.1016\/j.cmpb.2022.106666"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"102535","DOI":"10.1016\/j.media.2022.102535","article-title":"Adversarial multimodal fusion with attention mechanism for skin lesion classification using clinical and dermoscopic images","volume":"81","author":"Wang","year":"2022","journal-title":"Med. Image Anal."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"22115","DOI":"10.1007\/s00521-022-08084-6","article-title":"BF2SkNet: Best deep learning features fusion-assisted framework for multiclass skin lesion classification","volume":"35","author":"Ajmal","year":"2023","journal-title":"Neural Comput. Appl."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"180161","DOI":"10.1038\/sdata.2018.161","article-title":"Data descriptor: 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_38","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_39","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1504\/IJKEDM.2018.095523","article-title":"Machine Learning: The New \u2018Big Thing\u2019 for Competitive Advantage","volume":"5","author":"Attaran","year":"2018","journal-title":"Int. J. Knowl. Eng. Data Min."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Miko\u0142ajczyk, A., and Grochowski, M. (2018, January 9\u201312). Data augmentation for improving deep learning in image classification problem. Proceedings of the 2018 International Interdisciplinary PhD Workshop (IIPhDW), \u015awinou\u015bcie, Poland.","DOI":"10.1109\/IIPHDW.2018.8388338"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"012173","DOI":"10.1088\/1742-6596\/1693\/1\/012173","article-title":"An Overview of the Attention Mechanisms in Computer Vision","volume":"1693","author":"Yang","year":"2020","journal-title":"J. Phys. Conf. Ser."},{"key":"ref_42","unstructured":"Alche, M.N., Acevedo, D., and Mejail, M. (2021, January 11\u201317). EfficientARL: Improving skin cancer diagnoses by combining lightweight attention on EfficientNet. Proceedings of the 2021 IEEE\/CVF International Conference on Computer Vision Workshops (ICCVW), Montreal, BC, Canada."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"101765","DOI":"10.1016\/j.compmedimag.2020.101765","article-title":"Automatic skin lesion classification based on mid-level feature learning","volume":"84","author":"Liu","year":"2020","journal-title":"Comput. Med. Imaging Graph."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1016\/j.compmedimag.2018.10.007","article-title":"Fusing fine-tuned deep features for skin lesion classification","volume":"71","author":"Mahbod","year":"2019","journal-title":"Comput. Med. Imaging Graph."},{"key":"ref_45","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_46","unstructured":"Kingma, D.P., and Ba, J.L. (2015, January 7\u20139). Adam: A Method for Stochastic Optimization. Proceedings of the 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA."},{"key":"ref_47","unstructured":"Xu, B., Wang, N., Kong, H., Chen, T., and Li, M. (2015). Empirical Evaluation of Rectified Activations in Convolutional Network. arXiv."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"643","DOI":"10.1007\/s11517-021-02473-0","article-title":"InSiNet: A deep convolutional approach to skin cancer detection and segmentation","volume":"60","author":"Reis","year":"2022","journal-title":"Med. Biol. Eng. Comput."}],"container-title":["Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2078-2489\/16\/2\/120\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T16:29:21Z","timestamp":1760027361000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2078-2489\/16\/2\/120"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,2,8]]},"references-count":48,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2025,2]]}},"alternative-id":["info16020120"],"URL":"https:\/\/doi.org\/10.3390\/info16020120","relation":{},"ISSN":["2078-2489"],"issn-type":[{"value":"2078-2489","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,2,8]]}}}