{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,15]],"date-time":"2026-05-15T06:01:16Z","timestamp":1778824876058,"version":"3.51.4"},"reference-count":52,"publisher":"Springer Science and Business Media LLC","issue":"8","license":[{"start":{"date-parts":[[2022,5,31]],"date-time":"2022-05-31T00:00:00Z","timestamp":1653955200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,5,31]],"date-time":"2022-05-31T00:00:00Z","timestamp":1653955200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100001809","name":"national natural science foundation of china","doi-asserted-by":"publisher","award":["61773068"],"award-info":[{"award-number":["61773068"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"national natural science foundation of china","doi-asserted-by":"publisher","award":["61671141"],"award-info":[{"award-number":["61671141"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"the fundamental research funds for the central universities","award":["N2024005-1"],"award-info":[{"award-number":["N2024005-1"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Med Biol Eng Comput"],"published-print":{"date-parts":[[2022,8]]},"DOI":"10.1007\/s11517-022-02583-3","type":"journal-article","created":{"date-parts":[[2022,5,31]],"date-time":"2022-05-31T14:05:00Z","timestamp":1654005900000},"page":"2173-2188","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Automatic skin lesion classification using a new densely connected convolutional network with an SF module"],"prefix":"10.1007","volume":"60","author":[{"given":"Pufang","family":"Shan","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chong","family":"Fu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Liming","family":"Dai","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tihui","family":"Jia","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ming","family":"Tie","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jun","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,5,31]]},"reference":[{"issue":"2","key":"2583_CR1","first-page":"357","volume":"62","author":"AF Jerant","year":"2000","unstructured":"Jerant AF, Johnson JT, Sheridan CD, Caffrey TJ (2000) Early detection and treatment of skin cancer. American Family Physician 62(2):357\u2013368","journal-title":"American Family Physician"},{"issue":"1","key":"2583_CR2","first-page":"7","volume":"69","author":"RL Siegel","year":"2019","unstructured":"Siegel RL, Miller KD, Jemal A (2019) Cancer statistics, 2019. CA: A Cancer Journal for Clinicians 69(1):7\u201334","journal-title":"CA: A Cancer Journal for Clinicians"},{"issue":"5","key":"2583_CR3","doi-asserted-by":"publisher","first-page":"738","DOI":"10.1016\/S0190-9622(99)70010-1","volume":"41","author":"KA Freedberg","year":"1999","unstructured":"Freedberg KA, Geller AC, Miller DR, Lew RA, Koh HK (1999) Screening for malignant melanoma: a cost-effectiveness analysis. Journal of the American Academy of Dermatology 41(5):738\u2013745","journal-title":"Journal of the American Academy of Dermatology"},{"issue":"16","key":"2583_CR4","doi-asserted-by":"publisher","first-page":"3635","DOI":"10.1200\/JCO.2001.19.16.3635","volume":"19","author":"CM Balch","year":"2001","unstructured":"Balch CM, Buzaid AC, Soong SJ, Atkins MB, Cascinelli N, Coit DG, Fleming ID, Gershenwald JE, Houghton A Jr, Kirkwood JM et al (2001) Final version of the American joint committee on cancer staging system for cutaneous melanoma. Journal of Clinical Oncology 19(16):3635\u20133648","journal-title":"Journal of Clinical Oncology"},{"issue":"1","key":"2583_CR5","doi-asserted-by":"publisher","first-page":"109","DOI":"10.1016\/j.jaad.2001.11.001","volume":"52","author":"RP Braun","year":"2005","unstructured":"Braun RP, Rabinovitz HS, Oliviero M, Kopf AW, Saurat JH (2005) Dermoscopy of pigmented skin lesions. Journal of the American Academy of Dermatology 52(1):109\u2013121","journal-title":"Journal of the American Academy of Dermatology"},{"issue":"6","key":"2583_CR6","doi-asserted-by":"publisher","first-page":"362","DOI":"10.1016\/j.compmedimag.2007.01.003","volume":"31","author":"ME Celebi","year":"2007","unstructured":"Celebi ME, Kingravi HA, Uddin B, Iyatomi H, Aslandogan YA, Stoecker WV, Moss RH (2007) A methodological approach to the classification of dermoscopy images. Computerized Medical Imaging and Graphics 31(6):362\u2013373","journal-title":"Computerized Medical Imaging and Graphics"},{"issue":"4","key":"2583_CR7","doi-asserted-by":"publisher","first-page":"233","DOI":"10.1007\/s12293-014-0144-8","volume":"6","author":"G Schaefer","year":"2014","unstructured":"Schaefer G, Krawczyk B, Celebi ME, Iyatomi H (2014) An ensemble classification approach for melanoma diagnosis. Memetic Computing 6(4):233\u2013240","journal-title":"Memetic Computing"},{"issue":"8","key":"2583_CR8","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10916-019-1400-8","volume":"43","author":"A Murugan","year":"2019","unstructured":"Murugan A, Nair SAH, Kumar KS (2019) Detection of skin cancer using svm, random forest and knn classifiers. Journal of Medical Systems 43(8):1\u20139","journal-title":"Journal of Medical Systems"},{"key":"2583_CR9","doi-asserted-by":"crossref","unstructured":"Zaqout I (2019) Diagnosis of skin lesions based on dermoscopic images using image processing techniques. Pattern Recognition-Selected Methods and Applications","DOI":"10.5772\/intechopen.88065"},{"issue":"4","key":"2583_CR10","doi-asserted-by":"publisher","first-page":"484","DOI":"10.3390\/e22040484","volume":"22","author":"JA Almaraz-Damian","year":"2020","unstructured":"Almaraz-Damian JA, Ponomaryov V, Sadovnychiy S, Castillejos-Fernandez H (2020) Melanoma and nevus skin lesion classification using handcraft and deep learning feature fusion via mutual information measures. Entropy 22(4):484","journal-title":"Entropy"},{"key":"2583_CR11","doi-asserted-by":"crossref","unstructured":"Dhivyaa C, Sangeetha K, Balamurugan M, Amaran S, Vetriselvi T, Johnpaul P (2020) Skin lesion classification using decision trees and random forest algorithms. Journal of Ambient Intelligence and Humanized Computing pp 1\u201313","DOI":"10.1007\/s12652-020-02675-8"},{"issue":"11","key":"2583_CR12","doi-asserted-by":"publisher","first-page":"2278","DOI":"10.1109\/5.726791","volume":"86","author":"Y LeCun","year":"1998","unstructured":"LeCun Y, Bottou L, Bengio Y, Haffner P (1998) Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11):2278\u20132324","journal-title":"Proceedings of the IEEE"},{"key":"2583_CR13","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"key":"2583_CR14","doi-asserted-by":"publisher","first-page":"107413","DOI":"10.1016\/j.patcog.2020.107413","volume":"110","author":"C Barata","year":"2021","unstructured":"Barata C, Celebi ME, Marques JS (2021) Explainable skin lesion diagnosis using taxonomies. Pattern Recognition 110:107413","journal-title":"Pattern Recognition"},{"issue":"4","key":"2583_CR15","doi-asserted-by":"publisher","first-page":"991","DOI":"10.1109\/TMI.2018.2876510","volume":"38","author":"Y Xie","year":"2018","unstructured":"Xie Y, Xia Y, Zhang J, Song Y, Feng D, Fulham M, Cai W (2018) Knowledge-based collaborative deep learning for benign-malignant lung nodule classification on chest ct. IEEE Transactions on Medical Imaging 38(4):991\u20131004","journal-title":"IEEE Transactions on Medical Imaging"},{"key":"2583_CR16","doi-asserted-by":"crossref","unstructured":"Zhang J, Xie Y, Wu Q, Xia Y (2018) Skin lesion classification in dermoscopy images using synergic deep learning. In: International conference on medical image computing and computer-assisted intervention. Springer, pp 12\u201320","DOI":"10.1007\/978-3-030-00934-2_2"},{"key":"2583_CR17","doi-asserted-by":"crossref","unstructured":"Long J, Shelhamer E, Darrell T (2015) Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3431\u20133440","DOI":"10.1109\/CVPR.2015.7298965"},{"issue":"4","key":"2583_CR18","doi-asserted-by":"publisher","first-page":"834","DOI":"10.1109\/TPAMI.2017.2699184","volume":"40","author":"LC Chen","year":"2017","unstructured":"Chen LC, Papandreou G, Kokkinos I, Murphy K, Yuille AL (2017) Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs. IEEE transactions on pattern analysis and machine intelligence 40(4):834\u2013848","journal-title":"IEEE transactions on pattern analysis and machine intelligence"},{"key":"2583_CR19","doi-asserted-by":"crossref","unstructured":"He K, Gkioxari G, Doll\u00e1r P, Girshick R (2017) Mask r-cnn. In: Proceedings of the IEEE international conference on computer vision. pp 2961\u20132969","DOI":"10.1109\/ICCV.2017.322"},{"key":"2583_CR20","doi-asserted-by":"crossref","unstructured":"Lin TY, Goyal P, Girshick R, He K, Doll\u00e1r P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision. pp 2980\u20132988","DOI":"10.1109\/ICCV.2017.324"},{"issue":"4","key":"2583_CR21","doi-asserted-by":"publisher","first-page":"1006","DOI":"10.1109\/TBME.2018.2866166","volume":"66","author":"Z Yu","year":"2018","unstructured":"Yu Z, Jiang X, Zhou F, Qin J, Ni D, Chen S, Lei B, Wang T (2018) Melanoma recognition in dermoscopy images via aggregated deep convolutional features. IEEE Transactions on Biomedical Engineering 66(4):1006\u20131016","journal-title":"IEEE Transactions on Biomedical Engineering"},{"key":"2583_CR22","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1016\/j.compmedimag.2018.10.007","volume":"71","author":"A Mahbod","year":"2019","unstructured":"Mahbod A, Schaefer G, Ellinger I, Ecker R, Pitiot A, Wang C (2019) Fusing fine-tuned deep features for skin lesion classification. Computerized Medical Imaging and Graphics 71:19\u201329","journal-title":"Computerized Medical Imaging and Graphics"},{"issue":"9","key":"2583_CR23","doi-asserted-by":"publisher","first-page":"2092","DOI":"10.1109\/TMI.2019.2893944","volume":"38","author":"J Zhang","year":"2019","unstructured":"Zhang J, Xie Y, Xia Y, Shen C (2019) Attention residual learning for skin lesion classification. IEEE Transactions on Medical Imaging 38(9):2092\u20132103","journal-title":"IEEE Transactions on Medical Imaging"},{"issue":"5","key":"2583_CR24","doi-asserted-by":"publisher","first-page":"1325","DOI":"10.1007\/s10278-020-00371-9","volume":"33","author":"KM Hosny","year":"2020","unstructured":"Hosny KM, Kassem MA, Fouad MM (2020) Classification of skin lesions into seven classes using transfer learning with alexnet. Journal of Digital Imaging 33(5):1325\u20131334","journal-title":"Journal of Digital Imaging"},{"key":"2583_CR25","doi-asserted-by":"publisher","first-page":"104771","DOI":"10.1016\/j.compbiomed.2021.104771","volume":"137","author":"A Narin","year":"2021","unstructured":"Narin A (2021) Accurate detection of covid-19 using deep features based on x-ray images and feature selection methods. Computers in Biology and Medicine 137:104771","journal-title":"Computers in Biology and Medicine"},{"key":"2583_CR26","unstructured":"Razzak I, Naz S (2020) Unit-vise: Deep shallow unit-vise residual neural networks with transition layer for expert level skin cancer classification. IEEE\/ACM Transactions on Computational Biology and Bioinformatics"},{"issue":"4","key":"2583_CR27","doi-asserted-by":"publisher","first-page":"994","DOI":"10.1109\/TMI.2016.2642839","volume":"36","author":"L Yu","year":"2016","unstructured":"Yu L, Chen H, Dou Q, Qin J, Heng PA (2016) Automated melanoma recognition in dermoscopy images via very deep residual networks. IEEE Transactions on Medical Imaging 36(4):994\u20131004","journal-title":"IEEE Transactions on Medical Imaging"},{"key":"2583_CR28","doi-asserted-by":"publisher","first-page":"105351","DOI":"10.1016\/j.cmpb.2020.105351","volume":"190","author":"MA Al-Masni","year":"2020","unstructured":"Al-Masni MA, Kim DH, Kim TS (2020) Multiple skin lesions diagnostics via integrated deep convolutional networks for segmentation and classification. Computer Methods and Programs in Biomedicine 190:105351","journal-title":"Computer Methods and Programs in Biomedicine"},{"key":"2583_CR29","doi-asserted-by":"crossref","unstructured":"Huang G, Liu Z, Van Der\u00a0Maaten L, Weinberger KQ (2017) Densely connected convolutional networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp 4700\u20134708","DOI":"10.1109\/CVPR.2017.243"},{"key":"2583_CR30","doi-asserted-by":"publisher","first-page":"9872","DOI":"10.1109\/ACCESS.2018.2890127","volume":"7","author":"K Zhang","year":"2019","unstructured":"Zhang K, Guo Y, Wang X, Yuan J, Ding Q (2019) Multiple feature reweight densenet for image classification. IEEE Access 7:9872\u20139880","journal-title":"IEEE Access"},{"issue":"5","key":"2583_CR31","doi-asserted-by":"publisher","first-page":"779","DOI":"10.3390\/rs10050779","volume":"10","author":"Y Tao","year":"2018","unstructured":"Tao Y, Xu M, Lu Z, Zhong Y (2018) Densenet-based depth-width double reinforced deep learning neural network for high-resolution remote sensing image per-pixel classification. Remote Sensing 10(5):779","journal-title":"Remote Sensing"},{"issue":"8","key":"2583_CR32","doi-asserted-by":"publisher","first-page":"382","DOI":"10.3390\/genes9080382","volume":"9","author":"S Liang","year":"2018","unstructured":"Liang S, Zhang R, Liang D, Song T, Ai T, Xia C, Xia L, Wang Y (2018) Multimodal 3d densenet for idh genotype prediction in gliomas. Genes 9(8):382","journal-title":"Genes"},{"key":"2583_CR33","unstructured":"Iandola FN, Han S, Moskewicz MW, Ashraf K, Dally WJ, Keutzer K (2016) Squeezenet: Alexnet-level accuracy with 50x fewer parameters and $$<$$ 0.5 mb model size. arXiv:1602.07360"},{"key":"2583_CR34","doi-asserted-by":"crossref","unstructured":"Hu J, Shen L, Sun G (2018) Squeeze-and-excitation networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp 7132\u20137141","DOI":"10.1109\/CVPR.2018.00745"},{"key":"2583_CR35","doi-asserted-by":"crossref","unstructured":"Shan P, Wang Y, Fu C, Song W, Chen J (2020) Automatic skin lesion segmentation based on fc-dpn. Comput Biol Med 123:103762","DOI":"10.1016\/j.compbiomed.2020.103762"},{"key":"2583_CR36","doi-asserted-by":"crossref","unstructured":"Codella NC, Gutman D, Celebi ME, Helba B, Marchetti MA, Dusza SW, Kalloo A, Liopyris K, Mishra N, Kittler H, et\u00a0al (2018) 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). In: 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018). IEEE, pp 168\u2013172","DOI":"10.1109\/ISBI.2018.8363547"},{"issue":"1","key":"2583_CR37","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/sdata.2018.161","volume":"5","author":"P Tschandl","year":"2018","unstructured":"Tschandl P, Rosendahl C, Kittler H (2018) The ham10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions. Scientific Data 5(1):1\u20139","journal-title":"Scientific Data"},{"key":"2583_CR38","unstructured":"Codella N, Rotemberg V, Tschandl P, Celebi ME, Dusza S, Gutman D, Helba B, Kalloo A, Liopyris K, Marchetti M, et\u00a0al (2019) Skin lesion analysis toward melanoma detection 2018: A challenge hosted by the international skin imaging collaboration (isic). arXiv:1902.03368"},{"key":"2583_CR39","doi-asserted-by":"crossref","unstructured":"Mendon\u00e7a T, Ferreira PM, Marques JS, Marcal AR, Rozeira J (2013) Ph 2-a dermoscopic image database for research and benchmarking. In: 2013 35th annual international conference of the IEEE engineering in medicine and biology society (EMBC). IEEE, pp 5437\u20135440","DOI":"10.1109\/EMBC.2013.6610779"},{"key":"2583_CR40","unstructured":"Lin M, Chen Q, Yan S (2013) Network in network. arXiv:1312.4400"},{"key":"2583_CR41","unstructured":"Glorot X, Bordes A, Bengio Y (2011) Deep sparse rectifier neural networks. In: Proceedings of the fourteenth international conference on artificial intelligence and statistics. pp 315\u2013323"},{"key":"2583_CR42","doi-asserted-by":"crossref","unstructured":"Bottou L (2012) Stochastic gradient descent tricks. In: Neural networks: Tricks of the trade. Springer, pp 421\u2013436","DOI":"10.1007\/978-3-642-35289-8_25"},{"key":"2583_CR43","unstructured":"Gutman D, Codella NC, Celebi E, Helba B, Marchetti M, Mishra N, Halpern A (2016) Skin lesion analysis toward melanoma detection: A challenge at the international symposium on biomedical imaging (isbi) 2016, hosted by the international skin imaging collaboration (isic). arXiv:1605.01397"},{"key":"2583_CR44","unstructured":"Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In: Advances in neural information processing systems. pp 1097\u20131105"},{"key":"2583_CR45","doi-asserted-by":"crossref","unstructured":"Woo S, Park J, Lee JY, Kweon IS (2018) Cbam: Convolutional block attention module. In: Proceedings of the European conference on computer vision (ECCV). pp 3\u201319","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"2583_CR46","doi-asserted-by":"crossref","unstructured":"Selvaraju RR, Cogswell M, Das A, Vedantam R, Parikh D, Batra D (2017) Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE international conference on computer vision. pp 618\u2013626","DOI":"10.1109\/ICCV.2017.74"},{"key":"2583_CR47","unstructured":"Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv:1409.1556"},{"key":"2583_CR48","doi-asserted-by":"crossref","unstructured":"Chollet F (2017) Xception: Deep learning with depthwise separable convolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp 1251\u20131258","DOI":"10.1109\/CVPR.2017.195"},{"key":"2583_CR49","doi-asserted-by":"crossref","unstructured":"Xie S, Girshick R, Doll\u00e1r P, Tu Z, He K (2017) Aggregated residual transformations for deep neural networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp 1492\u20131500","DOI":"10.1109\/CVPR.2017.634"},{"key":"2583_CR50","unstructured":"Tan M, Le Q (2019) Efficientnet: Rethinking model scaling for convolutional neural networks. In: International Conference on Machine Learning, PMLR, pp 6105\u20136114"},{"key":"2583_CR51","doi-asserted-by":"crossref","unstructured":"Mahbod A, Schaefer G, Wang C, Ecker R, Ellinge I (2019) Skin lesion classification using hybrid deep neural networks. In: ICASSP 2019-2019 IEEE international conference on acoustics, speech and signal processing (ICASSP). IEEE, pp 1229\u20131233","DOI":"10.1109\/ICASSP.2019.8683352"},{"key":"2583_CR52","doi-asserted-by":"crossref","unstructured":"Harangi B (2018) Skin lesion classification with ensembles of deep convolutional neural networks. J Biomed Inform 86:25\u201332","DOI":"10.1016\/j.jbi.2018.08.006"}],"container-title":["Medical &amp; Biological Engineering &amp; Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11517-022-02583-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11517-022-02583-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11517-022-02583-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,7,19]],"date-time":"2022-07-19T03:42:47Z","timestamp":1658202167000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11517-022-02583-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,5,31]]},"references-count":52,"journal-issue":{"issue":"8","published-print":{"date-parts":[[2022,8]]}},"alternative-id":["2583"],"URL":"https:\/\/doi.org\/10.1007\/s11517-022-02583-3","relation":{},"ISSN":["0140-0118","1741-0444"],"issn-type":[{"value":"0140-0118","type":"print"},{"value":"1741-0444","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,5,31]]},"assertion":[{"value":"2 July 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 April 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"31 May 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}]}}