{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,7]],"date-time":"2026-11-07T09:27:48Z","timestamp":1794043668582,"version":"4.8.0"},"reference-count":58,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,11,4]],"date-time":"2024-11-04T00:00:00Z","timestamp":1730678400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2024,11,4]],"date-time":"2024-11-04T00:00:00Z","timestamp":1730678400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci Rep"],"DOI":"10.1038\/s41598-024-76852-6","type":"journal-article","created":{"date-parts":[[2024,11,4]],"date-time":"2024-11-04T04:26:21Z","timestamp":1730694381000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["CAD-PsorNet: deep transfer learning for computer-assisted diagnosis of skin psoriasis"],"prefix":"10.1038","volume":"14","author":[{"given":"Chandan","family":"Chakraborty","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Unmesh","family":"Achar","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sumit","family":"Nayek","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Arun","family":"Achar","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rashmi","family":"Mukherjee","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,11,4]]},"reference":[{"issue":"2","key":"76852_CR1","doi-asserted-by":"publisher","first-page":"377","DOI":"10.1038\/jid.2012.339","volume":"133","author":"R Parisi","year":"2013","unstructured":"Parisi, R., Symmons, D. P., Griffiths, C. E. & Ashcroft, D. M. Global epidemiology of psoriasis: a systematic review of incidence and prevalence. J. Invest. Dermatol.\u00a0133\u00a0(2), 377\u2013385. https:\/\/doi.org\/10.1038\/jid.2012.339 (2013).","journal-title":"J. Invest. Dermatol."},{"issue":"3","key":"76852_CR2","doi-asserted-by":"publisher","first-page":"e028116","DOI":"10.1136\/bmjopen-2018-028116","volume":"9","author":"A Egeberg","year":"2019","unstructured":"Egeberg, A., Andersen, Y. M. F. & Thyssen, J. P. Prevalence and characteristics of psoriasis in Denmark: findings from the Danish skin cohort. BMJ Open.\u00a09 (3), e028116. https:\/\/doi.org\/10.1136\/bmjopen-2018-028116 (2019).","journal-title":"BMJ open."},{"key":"76852_CR3","unstructured":"Okhandiar, R. P., & Banerjee, B. N. Psoriasis in the tropics: An epidemiological survey. J. Indian Med. Assoc. 41 (1), 16\u201320 (1963)."},{"issue":"6","key":"76852_CR4","doi-asserted-by":"publisher","first-page":"471","DOI":"10.4103\/2229-5178.193906","volume":"7","author":"S Dogra","year":"2016","unstructured":"Dogra, S. & Mahajan, R. Psoriasis: Epidemiology, clinical features, co-morbidities, and clinical scoring. Indian Dermatology Online J.\u00a07 (6), 471\u2013480. https:\/\/doi.org\/10.4103\/2229-5178.193906 (2016).","journal-title":"Indian Dermatology Online J."},{"issue":"3","key":"76852_CR5","doi-asserted-by":"publisher","first-page":"170","DOI":"10.7861\/clinmed.2021-0257","volume":"21","author":"A Raharja","year":"2021","unstructured":"Raharja, A., Mahil, S. K., Barker, J. N. & London Psoriasis: a brief overview. Clinical medicine (England), 21\u00a0(3), 170\u2013173. https:\/\/doi.org\/10.7861\/clinmed.2021-0257 (2021).","journal-title":"England)"},{"key":"76852_CR6","first-page":"1","volume":"11","author":"G Lomholt","year":"1964","unstructured":"Lomholt, G. Prevalence of skin diseases in a population; a census study from the Faroe Islands. Dan. Med. Bull.\u00a011, 1\u20137 (1964).","journal-title":"Dan. Med. Bull."},{"key":"76852_CR7","unstructured":"Hellgren, L. Psoriasis: The prevalence in sex, age, and occupational groups in the total population in Sweden. Morphology, inheritance, and association with other skin and rheumatic diseases. Acta Dermato-Venereologica.\u00a047 (Suppl. 47), 1\u201359 (1967)."},{"issue":"4","key":"76852_CR8","doi-asserted-by":"publisher","first-page":"344","DOI":"10.2340\/0001555561344346","volume":"61","author":"F Brandrup","year":"1981","unstructured":"Brandrup, F. & Green, A. The prevalence of psoriasis in Denmark. Acta dermato-venereologica. 61 (4), 344\u2013346 (1981).","journal-title":"Acta dermato-venereologica"},{"issue":"1","key":"76852_CR9","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1159\/000251595","volume":"148","author":"EM Farber","year":"1974","unstructured":"Farber, E. M. & Nall, L. The natural history of psoriasis in 5,600 patients. Dermatology. 148 (1), 1\u201318 (1974).","journal-title":"Dermatology"},{"issue":"5","key":"76852_CR10","doi-asserted-by":"publisher","first-page":"407","DOI":"10.1080\/02533839.2021.1919561","volume":"44","author":"A Bilal","year":"2021","unstructured":"Bilal, A., Sun, G. & Mazhar, S. Finger-vein recognition using a novel enhancement method with convolutional neural network. J. Chin. Inst. Eng.\u00a044 (5), 407\u2013417. https:\/\/doi.org\/10.1080\/02533839.2021.1919561 (2021).","journal-title":"J. Chin. Inst. Eng."},{"issue":"1","key":"76852_CR11","doi-asserted-by":"publisher","first-page":"e0295951","DOI":"10.1371\/journal.pone.0295951","volume":"19","author":"A Bilal","year":"2024","unstructured":"Bilal, A. et al. Improved support Vector Machine based on CNN-SVD for vision-threatening diabetic retinopathy detection and classification. PloS ONE. 19 (1), e0295951. https:\/\/doi.org\/10.1371\/journal.pone.0295951 (2024).","journal-title":"PloS One"},{"key":"76852_CR12","doi-asserted-by":"publisher","first-page":"108099","DOI":"10.1016\/j.compbiomed.2024.108099","volume":"171","author":"A Bilal","year":"2024","unstructured":"Bilal, A., Liu, X., Shafiq, M., Ahmed, Z. & Long, H. NIMEQ-SACNet: a novel self-attention precision medicine model for vision-threatening diabetic retinopathy using image data. Comput. Biol. Med.\u00a0171, 108099. https:\/\/doi.org\/10.1016\/j.compbiomed.2024.108099 (2024).","journal-title":"Comput. Biol. Med."},{"issue":"7","key":"76852_CR13","doi-asserted-by":"publisher","first-page":"1427","DOI":"10.3390\/sym14071427","volume":"14","author":"A Bilal","year":"2022","unstructured":"Bilal, A., Zhu, L., Deng, A., Lu, H. & Wu, N. AI-Based automatic detection and classification of Diabetic Retinopathy using U-Net and deep learning. Symmetry. 14 (7), 1427. https:\/\/doi.org\/10.3390\/sym14071427 (2022).","journal-title":"Symmetry"},{"issue":"24","key":"76852_CR14","doi-asserted-by":"publisher","first-page":"9603","DOI":"10.3390\/s22249603","volume":"22","author":"A Bilal","year":"2022","unstructured":"Bilal, A. et al. IGWO-IVNet3: DL-Based automatic diagnosis of lung nodules using an Improved Gray Wolf optimization and InceptionNet-V3. Sensors. 22 (24), 9603. https:\/\/doi.org\/10.3390\/s22249603 (2022).","journal-title":"Sensors"},{"issue":"1","key":"76852_CR15","doi-asserted-by":"publisher","first-page":"bbad481","DOI":"10.1093\/bib\/bbad481","volume":"25","author":"X Feng","year":"2024","unstructured":"Feng, X. et al. January, Advancing single-cell RNA-seq data analysis through the fusion of multi-layer perceptron and graph neural network. Brief. Bioinform.\u00a025, Issue 1, bbad481. (2024).","journal-title":"Brief. Bioinform."},{"key":"76852_CR16","doi-asserted-by":"publisher","first-page":"1377285","DOI":"10.3389\/fgene.2024.1377285","volume":"15","author":"X Yu","year":"2024","unstructured":"Yu, X. et al. iDNA-OpenPrompt: OpenPrompt learning model for identifying DNA methylation. Front. Genet.\u00a015, 1377285. https:\/\/doi.org\/10.3389\/fgene.2024.1377285 (2024).","journal-title":"Front. Genet."},{"key":"76852_CR17","doi-asserted-by":"publisher","first-page":"1166","DOI":"10.1007\/s42452-020-2963-1","volume":"2","author":"A Bilal","year":"2020","unstructured":"Bilal, A. & Sun, G. Neuro-optimized numerical solution of non-linear problem based on Flierl\u2013petviashivili equation. SN Appl. Sci.\u00a02, 1166. https:\/\/doi.org\/10.1007\/s42452-020-2963-1 (2020).","journal-title":"SN Appl. Sci."},{"issue":"2","key":"76852_CR18","doi-asserted-by":"publisher","first-page":"2401","DOI":"10.32604\/cmc.2023.037857","volume":"76","author":"A Bilal","year":"2023","unstructured":"Bilal, A., Liu, X., Long, H., Shafiq, M. & Waqar, M. Increasing crop quality and yield with a machine learning-based crop monitoring system. Computers Mater. Continua. 76 (2), 2401\u20132426. https:\/\/doi.org\/10.32604\/cmc.2023.037857 (2023).","journal-title":"Computers Mater. Continua"},{"key":"76852_CR19","doi-asserted-by":"publisher","first-page":"108483","DOI":"10.1016\/j.compbiomed.2024.108483","volume":"175","author":"A Bilal","year":"2024","unstructured":"Bilal, A. et al. BC-QNet: a quantum-infused ELM model for breast cancer diagnosis. Comput. Biol. Med.\u00a0175, 108483. https:\/\/doi.org\/10.1016\/j.compbiomed.2024.108483 (2024).","journal-title":"Comput. Biol. Med."},{"issue":"1","key":"76852_CR20","doi-asserted-by":"publisher","first-page":"10714","DOI":"10.1038\/s41598-024-61322-w","volume":"14","author":"A Bilal","year":"2024","unstructured":"Bilal, A. et al. Breast cancer diagnosis using support vector machine optimized by improved quantum inspired grey wolf optimization. Sci. Rep.\u00a014 (1), 10714. https:\/\/doi.org\/10.1038\/s41598-024-61322-w (2024).","journal-title":"Sci. Rep."},{"issue":"7","key":"76852_CR21","doi-asserted-by":"publisher","first-page":"1030","DOI":"10.3390\/math12071030","volume":"12","author":"A Naeem","year":"2024","unstructured":"Naeem, A. et al. SNC_Net: skin Cancer detection by integrating handcrafted and deep learning-based features using Dermoscopy images. Mathematics. 12 (7), 1030 (2024).","journal-title":"Mathematics"},{"issue":"3","key":"76852_CR22","doi-asserted-by":"publisher","first-page":"e0297667","DOI":"10.1371\/journal.pone.0297667","volume":"19","author":"A Naeem","year":"2024","unstructured":"Naeem, A. & Anees, T. DVFNet: a deep feature fusion-based model for the multiclassification of skin cancer utilizing dermoscopy images. Plos ONE, 19\u00a0(3), e0297667. (2024).","journal-title":"Plos one"},{"issue":"20","key":"76852_CR23","doi-asserted-by":"publisher","first-page":"8457","DOI":"10.3390\/s23208457","volume":"23","author":"S Riaz","year":"2023","unstructured":"Riaz, S., Naeem, A., Malik, H., Naqvi, R. A. & Loh, W. K. Federated and transfer learning methods for the classification of Melanoma and Nonmelanoma skin cancers: a prospective study. Sensors. 23 (20), 8457 (2023).","journal-title":"Sensors"},{"issue":"15","key":"76852_CR24","doi-asserted-by":"publisher","first-page":"5652","DOI":"10.3390\/s22155652","volume":"22","author":"A Naeem","year":"2022","unstructured":"Naeem, A., Anees, T., Fiza, M., Naqvi, R. A. & Lee, S. W. SCDNet: a deep learning-based framework for the multiclassification of skin cancer using dermoscopy images. Sensors. 22 (15), 5652 (2022).","journal-title":"Sensors"},{"issue":"02","key":"76852_CR25","first-page":"205","volume":"6","author":"A Naeem","year":"2024","unstructured":"Naeem, A. & Anees, T. A Multiclassification Framework for skin Cancer detection by the concatenation of Xception and ResNet101. J. Comput. Biomedical Inf.\u00a06 (02), 205\u2013227 (2024).","journal-title":"J. Comput. Biomedical Inf."},{"issue":"02","key":"76852_CR26","first-page":"1","volume":"4","author":"A Naeem","year":"2023","unstructured":"Naeem, A., Khan, A. H., u din Ayubi, S. & Malik, H. Predicting the Metastasis ability of prostate Cancer using machine learning classifiers. J. Comput. Biomedical Inf.\u00a04 (02), 1\u20137 (2023).","journal-title":"J. Comput. Biomedical Inf."},{"issue":"02","key":"76852_CR27","first-page":"195","volume":"5","author":"H Ayesha","year":"2023","unstructured":"Ayesha, H., Naeem, A., Khan, A. H., Abid, K. & Aslam, N. Multi-classification of skin Cancer using Multi-model Fusion technique. J. Comput. Biomedical Inf.\u00a05 (02), 195\u2013219 (2023).","journal-title":"J. Comput. Biomedical Inf."},{"key":"76852_CR28","doi-asserted-by":"publisher","first-page":"12870","DOI":"10.1109\/ACCESS.2023.3242666","volume":"11","author":"S Solanki","year":"2023","unstructured":"Solanki, S., Singh, U. P., Chouhan, S. S. & Jain, S. Brain tumor detection and classification using intelligence techniques: an overview. IEEE Access.\u00a011, 12870\u201312886 (2023).","journal-title":"IEEE Access."},{"issue":"8","key":"76852_CR29","doi-asserted-by":"publisher","first-page":"23929","DOI":"10.1007\/s11042-023-16430-6","volume":"83","author":"S Solanki","year":"2024","unstructured":"Solanki, S., Singh, U. P., Chouhan, S. S. & Jain, S. A systematic analysis of magnetic resonance images and deep learning methods used for diagnosis of brain tumor. Multimedia Tools Appl.\u00a083\u00a0(8), 23929\u201323966 (2024).","journal-title":"Multimedia Tools Appl."},{"issue":"1","key":"76852_CR30","doi-asserted-by":"publisher","first-page":"246","DOI":"10.1002\/ima.22797","volume":"33","author":"RK Patel","year":"2023","unstructured":"Patel, R. K. & Kashyap, M. Automated screening of glaucoma stages from retinal fundus images using BPS and LBP-based GLCM features. Int. J. Imaging Syst. Technol.\u00a033 (1), 246\u2013261 (2023).","journal-title":"Int. J. Imaging Syst. Technol."},{"key":"76852_CR31","doi-asserted-by":"crossref","unstructured":"Patel, R. K. & Kashyap, M. The study of various registration methods based on maximal stable extremal region and machine learning. Comput. Methods Biomech. Biomed. Eng.: Imaging Vis. 6, 1\u20138 (2023).","DOI":"10.1080\/21681163.2023.2243351"},{"key":"76852_CR32","doi-asserted-by":"publisher","first-page":"965423","DOI":"10.3389\/fmed.2022.965423","volume":"9","author":"Z Yu","year":"2022","unstructured":"Yu, Z., Kaizhi, S., Jianwen, H., Guanyu, Y. & Yonggang, W. A. A deep learning-based approach toward differentiating scalp psoriasis and seborrheic dermatitis from dermoscopic images. Front. Med.\u00a09, 965423. https:\/\/doi.org\/10.3389\/fmed.2022.965423 (2022).","journal-title":"Front. Med."},{"key":"76852_CR33","doi-asserted-by":"publisher","unstructured":"AlDera, S. A. & Othman, M. T. B. A Model for Classification and Diagnosis of Skin Disease using Machine Learning and Image Processing Techniques. Int. J. Adv. Comput. Sci. Appl. (IJACSA).\u00a013 (5), 2022.\u00a0https:\/\/doi.org\/10.14569\/IJACSA.2022.0130531\u00a0(2022).","DOI":"10.14569\/IJACSA.2022.0130531"},{"key":"76852_CR34","doi-asserted-by":"publisher","unstructured":"Aijaz, S. F., Khan, S. J., Azim, F., Shakeel, C. S. & Hassan, U. Deep Learning Application for Effective Classification of Different Types of Psoriasis. J. Healthcare Eng.\u00a02022, 7541583. https:\/\/doi.org\/10.1155\/2022\/7541583\u00a0(2022).","DOI":"10.1155\/2022\/7541583"},{"key":"76852_CR35","doi-asserted-by":"publisher","unstructured":"Li, F-L. et al. Deep learning in skin Disease Image Recognition: a review in IEEE Access.\u00a08, 208264\u2013208280. https:\/\/doi.org\/10.1109\/ACCESS.2020.3037258\u00a0(2020).","DOI":"10.1109\/ACCESS.2020.3037258"},{"issue":"4","key":"76852_CR36","doi-asserted-by":"publisher","first-page":"147","DOI":"10.1177\/2475530320950267","volume":"5","author":"K Yu","year":"2020","unstructured":"Yu, K., Syed, M. N., Bernardis, E. & Gelfand, J. M. Machine learning applications in the evaluation and management of Psoriasis: a systematic review. J. Psoriasis Psoriatic Arthritis. 5 (4), 147\u2013159. https:\/\/doi.org\/10.1177\/2475530320950267 (2020).","journal-title":"J. Psoriasis Psoriatic Arthritis"},{"issue":"2","key":"76852_CR37","doi-asserted-by":"publisher","first-page":"92","DOI":"10.1016\/j.dcmed.2021.06.003","volume":"4","author":"L Peng","year":"2021","unstructured":"Peng, L., Na, Y., Changsong, D., Sheng, L. & Hui, M. Research on classification diagnosis model of psoriasis based on deep residual network. Digit. Chin. Med.\u00a04 (2), 92\u2013101. https:\/\/doi.org\/10.1016\/j.dcmed.2021.06.003 (2021).","journal-title":"Digit. Chin. Med."},{"key":"76852_CR38","doi-asserted-by":"publisher","first-page":"163","DOI":"10.1007\/s11760-023-02722","volume":"18","author":"MS Rashid","year":"2024","unstructured":"Rashid, M. S. et al. Automated detection and classification of psoriasis types using deep neural networks from dermatology images. SIViP. 18, 163\u2013172. https:\/\/doi.org\/10.1007\/s11760-023-02722- (2024).","journal-title":"SIViP"},{"key":"76852_CR39","doi-asserted-by":"publisher","unstructured":"Yang, S., Zhou, D., Cao, J. & Guo, Y. LightingNet: An Integrated Learning Method for Low-Light Image Enhancement, in IEEE Transactions on Computational Imaging. 9, 29\u201342. https:\/\/doi.org\/10.1109\/TCI.2023.3240087\u00a0(2023).","DOI":"10.1109\/TCI.2023.3240087"},{"key":"76852_CR40","doi-asserted-by":"crossref","unstructured":"Litjens, G., Kooi, T., Bejnordi, B. E., Setio, A. A., Ciompi, F., Ghafoorian, M., Van der Laak, J. A., van Ginneken B. & S\u00e1nchez, CI. A survey on deep learning in medical image analysis. Medical Image Analysis.\u00a042, 60\u201388 (2017).","DOI":"10.1016\/j.media.2017.07.005"},{"issue":"9","key":"76852_CR41","doi-asserted-by":"publisher","first-page":"581","DOI":"10.21037\/atm.2020.04.39","volume":"8","author":"H Wu","year":"2020","unstructured":"Wu, H. et al. A deep learning, image based approach for automated diagnosis for inflammatory skin diseases. Annals Translational Med.\u00a08 (9), 581. https:\/\/doi.org\/10.21037\/atm.2020.04.39 (2020).","journal-title":"Annals Translational Med."},{"key":"76852_CR42","doi-asserted-by":"publisher","first-page":"104924","DOI":"10.1016\/j.compbiomed.2021.104924","volume":"139","author":"Y Yang","year":"2021","unstructured":"Yang, Y. et al. A convolutional neural network trained with dermoscopic images of psoriasis performed on par with 230 dermatologists. Comput. Biol. Med.\u00a0139, 104924. https:\/\/doi.org\/10.1016\/j.compbiomed.2021.104924 (2021).","journal-title":"Comput. Biol. Med."},{"key":"76852_CR43","doi-asserted-by":"publisher","unstructured":"Nieniewski, M., Chmielewski, L. J., Patrzyk, S. & Wozniacka, A. Studies in differentiating psoriasis from other dermatoses using small data set and transfer learning. J. Image Video Proc.\u00a02023 (7). https:\/\/doi.org\/10.1186\/s13640-023-00607-y (2023).","DOI":"10.1186\/s13640-023-00607-y"},{"issue":"3","key":"76852_CR44","doi-asserted-by":"publisher","first-page":"518","DOI":"10.1111\/jdv.15965","volume":"34","author":"S Zhao","year":"2020","unstructured":"Zhao, S. et al. Smart identification of psoriasis by images using convolutional neural networks: a case study in China. J. Eur. Acad. Dermatol. Venereol.: JEADV. 34 (3), 518\u2013524. https:\/\/doi.org\/10.1111\/jdv.15965 (2020).","journal-title":"J. Eur. Acad. Dermatology Venereology: JEADV"},{"issue":"8","key":"76852_CR45","doi-asserted-by":"publisher","first-page":"1314","DOI":"10.1111\/jdv.14960","volume":"32","author":"C Fink","year":"2018","unstructured":"Fink, C. et al. Intra- and interobserver variability of image-based PASI assessments in 120 patients suffering from plaque-type psoriasis. J. Eur. Acad. Dermatology Venereology: JEADV. 32 (8), 1314\u20131319. https:\/\/doi.org\/10.1111\/jdv.14960 (2018).","journal-title":"J. Eur. Acad. Dermatology Venereology: JEADV"},{"key":"76852_CR46","doi-asserted-by":"publisher","first-page":"106123","DOI":"10.1016\/j.cmpb.2021.106123","volume":"206","author":"R Raj","year":"2021","unstructured":"Raj, R., Londhe, N. D. & Sonawane, R. Automated psoriasis lesion segmentation from unconstrained environment using residual U-Net with transfer learning. Comput. Methods Programs Biomed.\u00a0206, 106123. https:\/\/doi.org\/10.1016\/j.cmpb.2021.106123 (2021).","journal-title":"Comput. Methods Programs Biomed."},{"key":"76852_CR47","doi-asserted-by":"publisher","first-page":"105300","DOI":"10.1016\/j.compbiomed.2022.105300","volume":"143","author":"KY Hsieh","year":"2022","unstructured":"Hsieh, K. Y. et al. A mask R-CNN based automatic assessment system for nail psoriasis severity. Comput. Biol. Med.\u00a0143, 105300. https:\/\/doi.org\/10.1016\/j.compbiomed.2022.105300 (2022).","journal-title":"Comput. Biol. Med."},{"key":"76852_CR48","doi-asserted-by":"publisher","first-page":"98","DOI":"10.1016\/j.cmpb.2015.11.013","volume":"126","author":"VK Shrivastava","year":"2016","unstructured":"Shrivastava, V. K., Londhe, N. D., Sonawane, R. S. & Suri, J. S. Computer-aided diagnosis of psoriasis skin images with HOS, texture and color features: a first comparative study of its kind. Comput. Methods Programs Biomed.\u00a0126, 98\u2013109. https:\/\/doi.org\/10.1016\/j.cmpb.2015.11.013 (2016).","journal-title":"Comput. Methods Programs Biomed."},{"key":"76852_CR49","doi-asserted-by":"publisher","unstructured":"Shtanko, A. & Kulik, S. Preliminary expriments on psoriasis classification in images. Proc. Comput. Sci.\u00a0213, 250\u2013254,2022. https:\/\/doi.org\/10.1016\/j.procs.2022.11.063\u00a0(2022).","DOI":"10.1016\/j.procs.2022.11.063"},{"key":"76852_CR50","doi-asserted-by":"publisher","unstructured":"Yaseliani, M. et al. Diagnostic clinical decision support based on deep learning and knowledge-based systems for Psoriasis: from diagnosis to Treatment options. Comput. Ind. Eng.\u00a0109754\u00a0https:\/\/doi.org\/10.1016\/j.cie.2023.109754 (2023).","DOI":"10.1016\/j.cie.2023.109754"},{"issue":"12","key":"76852_CR51","doi-asserted-by":"publisher","first-page":"2512","DOI":"10.1111\/jdv.18354","volume":"36","author":"T Okamoto","year":"2022","unstructured":"Okamoto, T., Kawai, M., Ogawa, Y., Shimada, S. & Kawamura, T. Artificial intelligence for the automated single-shot assessment of psoriasis severity. J. Eur. Acad. Dermatology Venereology: JEADV. 36 (12), 2512\u20132515. https:\/\/doi.org\/10.1111\/jdv.18354 (2022).","journal-title":"J. Eur. Acad. Dermatology Venereology: JEADV"},{"issue":"1","key":"76852_CR52","doi-asserted-by":"publisher","first-page":"68","DOI":"10.1111\/jdv.17711","volume":"36","author":"MJ Schaap","year":"2022","unstructured":"Schaap, M. J. et al. Image-based automated Psoriasis Area Severity Index scoring by Convolutional Neural Networks. J. Eur. Acad. Dermatology Venereology: JEADV. 36 (1), 68\u201375. https:\/\/doi.org\/10.1111\/jdv.17711 (2022).","journal-title":"J. Eur. Acad. Dermatology Venereology: JEADV"},{"key":"76852_CR53","doi-asserted-by":"publisher","unstructured":"Lin, Y. L., Huang, A., Yang, C. Y. & Chang, W. Y. Measurement of Body Surface Area for Psoriasis Using U-net Models. Comput Math Methods Med.\u00a02022, 7960151. https:\/\/doi.org\/10.1155\/2022\/7960151\u00a0(2022).","DOI":"10.1155\/2022\/7960151"},{"issue":"2","key":"76852_CR54","doi-asserted-by":"publisher","first-page":"577","DOI":"10.1109\/JBHI.2019.2910883","volume":"24","author":"Y George","year":"2020","unstructured":"George, Y., Aldeen, M. & Garnavi, R. Automatic Scale Severity Assessment Method in Psoriasis skin images using local descriptors. IEEE J. Biomedical Health Inf.\u00a024\u00a0(2), 577\u2013585. https:\/\/doi.org\/10.1109\/JBHI.2019.2910883 (2020).","journal-title":"IEEE J. Biomedical Health Inf."},{"key":"76852_CR55","doi-asserted-by":"publisher","first-page":"44","DOI":"10.1016\/j.compmedimag.2018.02.004","volume":"66","author":"Y George","year":"2018","unstructured":"George, Y., Aldeen, M. & Garnavi, R. Psoriasis image representation using patch-based dictionary learning for erythema severity scoring. Comput. Med. Imaging Graphics: Official J. Comput. Med. Imaging Soc.\u00a066, 44\u201355. https:\/\/doi.org\/10.1016\/j.compmedimag.2018.02.004 (2018).","journal-title":"Comput. Med. Imaging Graphics: Official J. Comput. Med. Imaging Soc."},{"key":"76852_CR56","doi-asserted-by":"publisher","unstructured":"Dash, M., Londhe, N. D., Ghosh, S., Raj, R. & Sonawane, R. S. A cascaded deep convolution neural network based CADx system for psoriasis lesion segmentation and severity assessment. Appl. Soft Comput.91 (106240). https:\/\/doi.org\/10.1016\/j.asoc.2020.106240 (2020).","DOI":"10.1016\/j.asoc.2020.106240"},{"key":"76852_CR57","doi-asserted-by":"publisher","unstructured":"He, K., Zhang, X., Ren, S., Sun, J. & Recognition, P. IEEE Conference on Computer Vision and Deep Residual Learning for Image Recognition, (CVPR), Las Vegas, NV, USA, 2016, pp. 770\u2013778. https:\/\/doi.org\/10.1109\/CVPR.2016.90\u00a0(2016).","DOI":"10.1109\/CVPR.2016.90"},{"key":"76852_CR58","doi-asserted-by":"publisher","first-page":"200","DOI":"10.1016\/j.procs.2016.07.014","volume":"90","author":"H Pratt","year":"2016","unstructured":"Pratt, H., Coenen, F., Broadbent, D. M., Harding, S. P. & Zheng, Y. Convolutional neural networks for diabetic retinopathy. Procedia Comput. Sci.\u00a090, 200\u2013205 (2016).","journal-title":"Procedia Comput. Sci."}],"container-title":["Scientific Reports"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41598-024-76852-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-024-76852-6","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-024-76852-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,4]],"date-time":"2024-11-04T04:26:45Z","timestamp":1730694405000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41598-024-76852-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,4]]},"references-count":58,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["76852"],"URL":"https:\/\/doi.org\/10.1038\/s41598-024-76852-6","relation":{},"ISSN":["2045-2322"],"issn-type":[{"value":"2045-2322","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,11,4]]},"assertion":[{"value":"1 June 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 October 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 November 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"During the preparation of this work Mr. Sumit had used Grammarly (free software version) in order to check the grammar used in the sentences and change them with their proper format in context to the raw draft of the author. After using this tool, Mr. Sumit reviews and edited the content of the publication as needed and takes(s) full responsibility for the content of the publication.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Generative AI and AI-assisted technologies in the writing process"}},{"value":"The authors declare no competing interests.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"26557"}}