{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,23]],"date-time":"2026-09-23T05:37:18Z","timestamp":1790141838727,"version":"4.0.1"},"reference-count":37,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2024,12,28]],"date-time":"2024-12-28T00:00:00Z","timestamp":1735344000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"F\u0131rat University Scientific Research Projects Support Programme","award":["MF.24.20"],"award-info":[{"award-number":["MF.24.20"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Background: Psoriasis is a chronic, immune-mediated skin disease characterized by lifelong persistence and fluctuating symptoms. The clinical similarities among its subtypes and the diversity of symptoms present challenges in diagnosis. Early diagnosis plays a vital role in preventing the spread of lesions and improving patients\u2019 quality of life. Methods: This study proposes a hybrid model combining multiple transfer learning and ensemble learning methods to classify psoriasis subtypes accurately and efficiently. The dataset includes 930 images labeled by expert dermatologists from the Dermatology Clinic of F\u0131rat University Hospital, representing four distinct subtypes: generalized, guttate, plaque, and pustular. Class imbalance was addressed by applying synthetic data augmentation techniques, particularly for the rare subtype. To reduce the influence of nonlesion environmental factors, the images underwent systematic cropping and preprocessing steps, such as Gaussian blur, thresholding, morphological operations, and contour detection. DenseNet-121, EfficientNet-B0, and ResNet-50 transfer learning models were utilized to extract feature vectors, which were then combined to form a unified feature set representing the strengths of each model. The feature set was divided into 80% training and 20% testing subsets and evaluated using a hard voting classifier consisting of logistic regression, random forest, support vector classifier, k-nearest neighbors, and gradient boosting algorithms. Results: The proposed hybrid approach achieved 93.14% accuracy, 96.75% precision, and an F1 score of 91.44%, demonstrating superior performance compared to individual transfer learning models. Conclusions: This method offers significant potential to enhance the classification of psoriasis subtypes in clinical and real-world settings.<\/jats:p>","DOI":"10.3390\/diagnostics15010055","type":"journal-article","created":{"date-parts":[[2024,12,31]],"date-time":"2024-12-31T14:21:12Z","timestamp":1735654872000},"page":"55","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["A Hybrid Model for Psoriasis Subtype Classification: Integrating Multi Transfer Learning and Hard Voting Ensemble Models"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2038-4043","authenticated-orcid":false,"given":"\u0130smail An\u0131l","family":"Avc\u0131","sequence":"first","affiliation":[{"name":"Department of Electrical-Electronics Engineering, Faculty of Technology, Firat University, 23200 Elazig, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1130-2499","authenticated-orcid":false,"given":"Merve","family":"Zirekg\u00fcr","sequence":"additional","affiliation":[{"name":"Department of Electrical and Electronics Engineering, Faculty of Engineering and Natural Sciences, Malatya Turgut Ozal University, 44200 Malatya, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7995-3901","authenticated-orcid":false,"given":"Bar\u0131\u015f","family":"Karakaya","sequence":"additional","affiliation":[{"name":"Department of Electrical-Electronics Engineering, Faculty of Engineering, Firat University, 23200 Elazig, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bet\u00fcl","family":"Demir","sequence":"additional","affiliation":[{"name":"Department of Dermatology, Faculty of Medicine, Firat University, 23200 Elazig, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,12,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"496","DOI":"10.1056\/NEJMra0804595","article-title":"Psoriasis","volume":"361","author":"Nestle","year":"2009","journal-title":"N. Engl. J. Med."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"263","DOI":"10.1016\/S0140-6736(07)61128-3","article-title":"Pathogenesis and clinical features of psoriasis","volume":"370","author":"Griffiths","year":"2007","journal-title":"Lancet"},{"key":"ref_3","first-page":"686","article-title":"The clinical and immunological features of guttate psoriasis","volume":"23","author":"Tobin","year":"2005","journal-title":"Clin. Dermatol."},{"key":"ref_4","unstructured":"World Health Organization (2016). Global Report on Psoriasis, World Health Organization."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P., and Aroyo, L.M. (2021, January 8\u201313). \u201cEveryone wants to do the model work, not the data work\u201d: Data Cascades in High-Stakes AI. Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems, Yokohama, Japan.","DOI":"10.1145\/3411764.3445518"},{"key":"ref_6","unstructured":"Tan, M., and Le, Q. (2019, January 9\u201315). EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks. Proceedings of the 36th International Conference on Machine Learning, Proceedings of Machine Learning Research, Long Beach, CA, USA."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Dietterich, T.G. (2000). Ensemble Methods in Machine Learning, Springer.","DOI":"10.1007\/3-540-45014-9_1"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Syu, J.-M., Lai, C.-H., Lin, G.-S., and Chai, S.-K. (2018, January 19\u201321). Psoriasis detection based on deep neural network. Proceedings of the 2018 IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW), Taichung, Taiwan.","DOI":"10.1109\/ICCE-China.2018.8448609"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1016\/j.cmpb.2018.01.027","article-title":"Psoriasis skin biopsy image segmentation using Deep Convolutional Neural Network","volume":"159","author":"Pal","year":"2018","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"93","DOI":"10.21015\/vtse.v9i3.686","article-title":"Automated psoriasis detection using deep learning","volume":"9","author":"Amin","year":"2021","journal-title":"VFAST Trans. Softw. Eng."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"7541583","DOI":"10.1155\/2022\/7541583","article-title":"Deep learning application for effective classification of different types of psoriasis","volume":"2022","author":"Aijaz","year":"2022","journal-title":"J. Healthc. Eng."},{"key":"ref_12","unstructured":"Goswami, P., Singh, S., and Sharma, N. (2023). Intra-Class Classification of Psoriasis Using Deep Learning. J. Biomed. Eng. Soc. India, 18."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"318","DOI":"10.17485\/IJST\/v16i5.1723","article-title":"A Machine Learning Approach for the Estimation of Severity of Psoriasis Disorder Using Depth-Wise Convolution Neural Network","volume":"16","author":"Arunkumar","year":"2023","journal-title":"Indian J. Sci. Technol."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"109754","DOI":"10.1016\/j.cie.2023.109754","article-title":"Diagnostic clinical decision support based on deep learning and knowledge-based systems for psoriasis: From diagnosis to treatment options","volume":"187","author":"Yaseliani","year":"2024","journal-title":"Comput. Ind. Eng."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1186\/s13640-023-00607-y","article-title":"Studies in differentiating psoriasis from other dermatoses using small data set and transfer learning","volume":"2023","author":"Nieniewski","year":"2023","journal-title":"EURASIP J. Image Video Process."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Moon, C.-I., Lee, J., Baek, Y.S., and Lee, O. (2023). Psoriasis severity classification based on adaptive multi-scale features for multi-severity disease. Sci. Rep., 13.","DOI":"10.1038\/s41598-023-44478-9"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Szeliski, R. (2022). Computer Vision: Algorithms and Applications, Springer Nature.","DOI":"10.1007\/978-3-030-34372-9"},{"key":"ref_18","unstructured":"Gonzalez, R.C., and Woods, R.E. (2002). Digital Image Processing, Prentice Hall."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Burger, W., and Burge, M.J. (2022). Digital Image Processing: An Algorithmic Introduction, Springer Nature.","DOI":"10.1007\/978-3-031-05744-1"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Singh, G., Agrawal, S., and Sohi, B. (2020). Hybrid L+ C Edge Detection Algorithm for Harnessing Large Image Datasets. Electronic Systems and Intelligent Computing: Proceedings of ESIC 2020, Springer.","DOI":"10.1007\/978-981-15-7031-5_44"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Banik, D., and Bhattacharjee, D. (2023). Mitigating data imbalance issues in medical image analysis. Research Anthology on Improving Medical Imaging Techniques for Analysis and Intervention, IGI Global.","DOI":"10.4018\/978-1-6684-7544-7.ch063"},{"key":"ref_22","unstructured":"Li, C., and Jiang, X. (2023). Experimenting with an Evaluation Framework for Imbalanced Data Learning (EFIDL). arXiv."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"893","DOI":"10.1002\/jvc2.233","article-title":"Prevalence and incidence of patients with generalized pustular psoriasis in Denmark\u2014A nationwide registry-based study","volume":"2","author":"Haugaard","year":"2023","journal-title":"JEADV Clin. Pract."},{"key":"ref_24","unstructured":"Goodfellow, I. (2016). Deep Learning, MIT Press."},{"key":"ref_25","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, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., and Fei-Fei, L. (2009, January 20\u201325). Imagenet: A large-scale hierarchical image database. Proceedings of the 2009 IEEE Conference on Computer Vision and Pattern Recognition, Miami, FL, USA.","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"ref_28","unstructured":"Li, Z., and Arora, S. (2019). An exponential learning rate schedule for deep learning. arXiv."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1109\/JPROC.2020.3004555","article-title":"A comprehensive survey on transfer learning","volume":"109","author":"Zhuang","year":"2020","journal-title":"Proc. IEEE"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1345","DOI":"10.1109\/TKDE.2009.191","article-title":"A survey on transfer learning","volume":"22","author":"Pan","year":"2009","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Hoang, V.-T., and Jo, K.-H. (2021, January 8\u201310). Practical analysis on architecture of EfficientNet. Proceedings of the 2021 14th International Conference on Human System Interaction (HSI), Gda\u0144sk, Poland.","DOI":"10.1109\/HSI52170.2021.9538782"},{"key":"ref_32","unstructured":"Panigrahi, A., Chen, Y., and Kuo, C.-C.J. (2018). Analysis on gradient propagation in batch normalized residual networks. arXiv."},{"key":"ref_33","unstructured":"Pleiss, G., Chen, D., Huang, G., Li, T., Van Der Maaten, L., and Weinberger, K.Q. (2017). Memory-efficient implementation of densenets. arXiv."},{"key":"ref_34","unstructured":"Wightman, R., Touvron, H., and J\u00e9gou, H. (2021). Resnet strikes back: An improved training procedure in timm. arXiv."},{"key":"ref_35","unstructured":"Lin, M., Chen, Q., and Yan, S. (2013, January 2\u20134). Network in Network. Proceedings of the International Conference on Learning Representations (ICLR), Scottsdale, Arizona."},{"key":"ref_36","first-page":"1929","article-title":"Dropout: A simple way to prevent neural networks from overfitting","volume":"15","author":"Srivastava","year":"2014","journal-title":"J. Mach. Learn. Res."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Kuncheva, L.I. (2014). Combining Pattern Classifiers: Methods and Algorithms, John Wiley & Sons.","DOI":"10.1002\/9781118914564"}],"container-title":["Diagnostics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2075-4418\/15\/1\/55\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T16:55:32Z","timestamp":1760115332000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2075-4418\/15\/1\/55"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,28]]},"references-count":37,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,1]]}},"alternative-id":["diagnostics15010055"],"URL":"https:\/\/doi.org\/10.3390\/diagnostics15010055","relation":{},"ISSN":["2075-4418"],"issn-type":[{"value":"2075-4418","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,12,28]]}}}