{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,11]],"date-time":"2024-09-11T07:09:12Z","timestamp":1726038552783},"reference-count":48,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2019,7,1]],"date-time":"2019-07-01T00:00:00Z","timestamp":1561939200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Biomedical Signal Processing and Control"],"published-print":{"date-parts":[[2019,7]]},"DOI":"10.1016\/j.bspc.2019.04.002","type":"journal-article","created":{"date-parts":[[2019,5,9]],"date-time":"2019-05-09T00:24:31Z","timestamp":1557361471000},"page":"226-237","update-policy":"http:\/\/dx.doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":74,"special_numbering":"C","title":["PsLSNet: Automated psoriasis skin lesion segmentation using modified U-Net-based fully convolutional network"],"prefix":"10.1016","volume":"52","author":[{"given":"Manoranjan","family":"Dash","sequence":"first","affiliation":[]},{"ORCID":"http:\/\/orcid.org\/0000-0002-6320-5746","authenticated-orcid":false,"given":"Narendra D.","family":"Londhe","sequence":"additional","affiliation":[]},{"given":"Subhojit","family":"Ghosh","sequence":"additional","affiliation":[]},{"given":"Ashish","family":"Semwal","sequence":"additional","affiliation":[]},{"given":"Rajendra S.","family":"Sonawane","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"issue":"12","key":"10.1016\/j.bspc.2019.04.002_bib0005","doi-asserted-by":"crossref","first-page":"1559","DOI":"10.1001\/archderm.143.12.1559","article-title":"Incidence, and risk factors for psoriasis in the general population","volume":"143","author":"Huerta","year":"2007","journal-title":"Arch. Dermatol."},{"key":"10.1016\/j.bspc.2019.04.002_bib0010","doi-asserted-by":"crossref","first-page":"422","DOI":"10.1016\/0190-9622(93)70062-X","article-title":"The annual cost of psoriasis","volume":"28","author":"Sander","year":"1993","journal-title":"J. Am. Acad. Dermatol."},{"key":"10.1016\/j.bspc.2019.04.002_bib0015","doi-asserted-by":"crossref","first-page":"422","DOI":"10.1016\/0190-9622(93)70062-X","article-title":"The annual cost of psoriasis","volume":"28","author":"Sander","year":"1993","journal-title":"J. Am. Acad. Dermatol."},{"issue":"June","key":"10.1016\/j.bspc.2019.04.002_bib0020","article-title":"Psoriasis in hospital population","author":"Adam","year":"1980","journal-title":"Med. J. Malay."},{"key":"10.1016\/j.bspc.2019.04.002_bib0025","series-title":"Malaysian Patients Knowledge of Psoriasis: Psoriasis Association Members Vs Non-Members","author":"Yee","year":"1999"},{"key":"10.1016\/j.bspc.2019.04.002_bib0030","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1016\/j.compbiomed.2015.05.005","article-title":"First review on psoriasis severity risk stratification: an engineering perspective","volume":"63","author":"Shrivastava","year":"2015","journal-title":"Comput. Biol. Med."},{"key":"10.1016\/j.bspc.2019.04.002_bib0035","series-title":"Handbook of Psoriasis","first-page":"2004","article-title":"Psoriasis, Marcel Dekker","author":"Roenigk","year":"1985"},{"key":"10.1016\/j.bspc.2019.04.002_bib0040","doi-asserted-by":"crossref","first-page":"463","DOI":"10.1007\/s004030050338","article-title":"Genetics of psoriasis","volume":"290","author":"Henseler","year":"1998","journal-title":"Arch. Dermatol. Res."},{"issue":"2","key":"10.1016\/j.bspc.2019.04.002_bib0045","first-page":"65","article-title":"Psoriasis assessment tools in clinical trials","volume":"64","author":"Feldman","year":"2005","journal-title":"Ann. Rheum. Dis."},{"key":"10.1016\/j.bspc.2019.04.002_bib0050","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1023\/A:1023379719594","article-title":"Neuro-Fuzzy approach to the segmentation of psoriasis images","volume":"35","author":"Taur","year":"2003","journal-title":"J. VLSI Signal Process. Syst. Signal Image Video Technol."},{"issue":"2","key":"10.1016\/j.bspc.2019.04.002_bib0055","doi-asserted-by":"crossref","first-page":"390","DOI":"10.1109\/TSMCB.2005.859935","article-title":"Segmentation of psoriasis vulgari images using multiresolution-based orthogonal subspace techniques","volume":"36","author":"Taur","year":"2006","journal-title":"IEEE Trans. Syst. Man Cybernet, Part B: Cybernet."},{"issue":"6","key":"10.1016\/j.bspc.2019.04.002_bib0060","doi-asserted-by":"crossref","first-page":"648","DOI":"10.3844\/jcssp.2010.648.652","article-title":"Psoriasis detection using color and texture features","volume":"6","author":"Nidhal","year":"2010","journal-title":"J. Comput. Sci."},{"key":"10.1016\/j.bspc.2019.04.002_bib0065","doi-asserted-by":"crossref","first-page":"895","DOI":"10.1016\/j.measurement.2011.02.006","article-title":"Psoriasis image identification using k-means clustering with morphological processing","volume":"44","author":"Li-Hong","year":"2011","journal-title":"Measurement"},{"key":"10.1016\/j.bspc.2019.04.002_bib0070","article-title":"Psoriasis segmentation through chromatic regions and geometric active contours","volume":"28","author":"Bogo","year":"2012","journal-title":"International Conference of the IEEE EMBS"},{"key":"10.1016\/j.bspc.2019.04.002_bib0075","series-title":"International Conference on Optical Instruments and Technology","first-page":"9045","article-title":"Easy interactive and quick psoriasis lesion segmentation","author":"Guoli","year":"2013"},{"issue":"4","key":"10.1016\/j.bspc.2019.04.002_bib0080","doi-asserted-by":"crossref","first-page":"719","DOI":"10.1109\/TMI.2012.2236349","article-title":"Automatic segmentation of scaling in 2-D psoriasis skin images","volume":"32","author":"Lu","year":"2013","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"205","key":"10.1016\/j.bspc.2019.04.002_bib0085","first-page":"1","article-title":"Measurement of psoriasis area and severity index area score of indian psoriasis patients","volume":"5","author":"Shrivastava","year":"2019","journal-title":"J. Med. Imaging Health Inf."},{"key":"10.1016\/j.bspc.2019.04.002_bib0090","doi-asserted-by":"crossref","first-page":"1012","DOI":"10.1016\/j.patcog.2012.08.012","article-title":"Automatic segmentation of dermoscopy images using self Generating neural networks seeded by genetic algorithm","volume":"46","author":"Xie","year":"2013","journal-title":"Patt. Recogn."},{"key":"10.1016\/j.bspc.2019.04.002_bib0095","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1016\/j.cmpb.2017.07.011","article-title":"A novel and robust Bayesian approach for segmentation of psoriasis lesions and its risk stratification: inter-comparison of nine systems","volume":"150","author":"Shrivastava","year":"2017","journal-title":"Comput. Methods Programs Biomed."},{"key":"10.1016\/j.bspc.2019.04.002_bib0100","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Review on deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"10.1016\/j.bspc.2019.04.002_bib0105","doi-asserted-by":"crossref","first-page":"9375","DOI":"10.1109\/ACCESS.2017.2788044","article-title":"Deep learning applications in medical image analysis","volume":"6","author":"Ker","year":"2018","journal-title":"IEEE Access"},{"key":"10.1016\/j.bspc.2019.04.002_bib0110","series-title":"IEEE International Conference on Soft Computing and Measurements (SCM)","article-title":"Application of modern architectures of deep neural networks for solving practical problems","author":"Mityakov","year":"2017"},{"issue":"11","key":"10.1016\/j.bspc.2019.04.002_bib0115","doi-asserted-by":"crossref","first-page":"2319","DOI":"10.1109\/TMI.2017.2721362","article-title":"Auto-context convolutional neural network (Auto- net) for brain extraction in magnetic resonance imaging","volume":"36","author":"Salehi","year":"2017","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"9","key":"10.1016\/j.bspc.2019.04.002_bib0120","doi-asserted-by":"crossref","first-page":"01876","DOI":"10.1109\/TMI.2017.2695227","article-title":"Automatic skin lesion segmentation using deep fully convolutional networks with jaccard distance","volume":"36","author":"Yuan","year":"2017","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.bspc.2019.04.002_bib0125","doi-asserted-by":"crossref","DOI":"10.1109\/TMI.2018.2845918","article-title":"H-DenseUNet: hybrid densely connected UNet for liver and tumor segmentation from CT volumes","author":"Li","year":"2018","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.bspc.2019.04.002_bib0130","doi-asserted-by":"crossref","DOI":"10.1109\/TMI.2018.2806086","article-title":"Fully convolutional architectures for multi-class segmentation in chest radiographs","author":"Novikov","year":"2018","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"4","key":"10.1016\/j.bspc.2019.04.002_bib0135","doi-asserted-by":"crossref","first-page":"640","DOI":"10.1109\/TPAMI.2016.2572683","article-title":"Fully convolutional networks for semantic segmentation","volume":"39","author":"Long","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"9","key":"10.1016\/j.bspc.2019.04.002_bib0140","doi-asserted-by":"crossref","first-page":"1876","DOI":"10.1109\/TMI.2017.2695227","article-title":"Automatic skin lesion segmentation using deep fully convolutional networks with jaccard distance","volume":"36","author":"Yuan","year":"2017","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.bspc.2019.04.002_bib0145","first-page":"234","article-title":"U-net: convolutional networks for biomedical image segmentation","volume":"2015","author":"Ronneberger","year":"2015","journal-title":"International Conference on Medical Image Computing and Computer-Assisted Intervention MICCAI"},{"key":"10.1016\/j.bspc.2019.04.002_bib0150","series-title":"20th International Conference on Artificial Neural Networks (ICANN)","article-title":"Evaluation of pooling operations in convolutional architectures for object recognition","author":"Scherer","year":"2010"},{"key":"10.1016\/j.bspc.2019.04.002_bib0155","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1016\/j.cmpb.2018.01.025","article-title":"Nifty net: a deep-learning plarform for medical imaging","volume":"158","author":"Gibson","year":"2018","journal-title":"Comput. Methods Programs Biomed."},{"issue":"8","key":"10.1016\/j.bspc.2019.04.002_bib0160","doi-asserted-by":"crossref","first-page":"1865","DOI":"10.1109\/TMI.2018.2806086","article-title":"Fully convolutional architectures for multiclass segmentation in chest radiographs","volume":"37","author":"Novikov","year":"2018","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.bspc.2019.04.002_bib0165","doi-asserted-by":"crossref","DOI":"10.1038\/s41598-018-24304-3","article-title":"Spinal cord gray matter segmentation using deep dilated convolutions","author":"Perone","year":"2018","journal-title":"Sci. Rep."},{"key":"10.1016\/j.bspc.2019.04.002_bib0170","article-title":"Fully convolutional multi-scale residual DenseNets for cardiac segmentation and automated cardiac diagnosis using ensemble of classifiers","author":"Kheneda","year":"2018","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.bspc.2019.04.002_bib0175","series-title":"\u201cKeras: The Python Deep Learning library.\u201d","author":"Chollet","year":"2015"},{"key":"10.1016\/j.bspc.2019.04.002_bib0180","series-title":"12th USENIX Symposium on Operating Systems Design and Implementation (OSDI 16)","first-page":"265","article-title":"TensorFlow: a system for large-scale machine learning","author":"Abadi","year":"2016"},{"key":"10.1016\/j.bspc.2019.04.002_bib0185","series-title":"The OpenCV Library","author":"Bradski","year":"2000"},{"key":"10.1016\/j.bspc.2019.04.002_bib0190","first-page":"249","article-title":"\u201cUnderstanding the difficulty of training deep feedforward neural networks","volume":"9","author":"Glorot","year":"2010","journal-title":"Proc. Aistats"},{"key":"10.1016\/j.bspc.2019.04.002_bib0195","first-page":"421","volume":"vol. 7700","author":"Bottou","year":"2012"},{"key":"10.1016\/j.bspc.2019.04.002_bib0200","first-page":"448","article-title":"Batch normalization: accelerating deep network training by reducing internal covariant shift","volume":"3","author":"Ioffe","year":"2015","journal-title":"Mach. Learn."},{"key":"10.1016\/j.bspc.2019.04.002_bib0205","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":"10.1016\/j.bspc.2019.04.002_bib0210","series-title":"Digital Image Processing","author":"Gonzalez","year":"2008"},{"key":"10.1016\/j.bspc.2019.04.002_bib0215","series-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","article-title":"Fully convolutional networks for semantic segmentation","author":"Long","year":"2015"},{"key":"10.1016\/j.bspc.2019.04.002_bib0220","series-title":"Segnet: a Deep Convolutional Encoder-decoder Architecture for Image Segmentation","author":"Badrinarayanan","year":"2015"},{"key":"10.1016\/j.bspc.2019.04.002_bib0225","doi-asserted-by":"crossref","DOI":"10.1109\/TMI.2018.2845918","article-title":"H-DenseUNet: hybrid densely connected UNet for liver and tumor segmentation from CT volumes","author":"Li","year":"2018","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.bspc.2019.04.002_bib0230","series-title":"Joint Optic Disc and Cup Segmentation Based on Multi Label Deep Network and Polar Transformation","author":"Fu","year":"2018"},{"key":"10.1016\/j.bspc.2019.04.002_bib0235","series-title":"A Deep Residual Architecture for Skin Lesion Segmentation, OR 2.0 Context- Aware Operating Theaters, Computer Assisted Robotic Endoscopy, Clinical Image-Based Procedures, and Skin Image Analysis","first-page":"277","author":"Venkatesh","year":"2018"},{"key":"10.1016\/j.bspc.2019.04.002_bib0240","article-title":"Do deep nets really need to be deep","author":"Jimmy","year":"2014","journal-title":"Adv. Neural Inf. Process. Syst."}],"container-title":["Biomedical Signal Processing and Control"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1746809419300990?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1746809419300990?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2019,6,13]],"date-time":"2019-06-13T23:46:27Z","timestamp":1560469587000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1746809419300990"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,7]]},"references-count":48,"alternative-id":["S1746809419300990"],"URL":"https:\/\/doi.org\/10.1016\/j.bspc.2019.04.002","relation":{},"ISSN":["1746-8094"],"issn-type":[{"value":"1746-8094","type":"print"}],"subject":[],"published":{"date-parts":[[2019,7]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"PsLSNet: Automated psoriasis skin lesion segmentation using modified U-Net-based fully convolutional network","name":"articletitle","label":"Article Title"},{"value":"Biomedical Signal Processing and Control","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.bspc.2019.04.002","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2019 Elsevier Ltd. All rights reserved.","name":"copyright","label":"Copyright"}]}}