{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,28]],"date-time":"2026-07-28T03:20:08Z","timestamp":1785208808837,"version":"3.55.0"},"reference-count":39,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2022,9,1]],"date-time":"2022-09-01T00:00:00Z","timestamp":1661990400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2022,9,1]],"date-time":"2022-09-01T00:00:00Z","timestamp":1661990400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2022,9,1]],"date-time":"2022-09-01T00:00:00Z","timestamp":1661990400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2022,9,1]],"date-time":"2022-09-01T00:00:00Z","timestamp":1661990400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2022,9,1]],"date-time":"2022-09-01T00:00:00Z","timestamp":1661990400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2022,9,1]],"date-time":"2022-09-01T00:00:00Z","timestamp":1661990400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2022,9,1]],"date-time":"2022-09-01T00:00:00Z","timestamp":1661990400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"content-domain":{"domain":["clinicalkey.com","clinicalkey.com.au","clinicalkey.es","clinicalkey.fr","clinicalkey.jp","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Computer Methods and Programs in Biomedicine"],"published-print":{"date-parts":[[2022,9]]},"DOI":"10.1016\/j.cmpb.2022.107019","type":"journal-article","created":{"date-parts":[[2022,7,10]],"date-time":"2022-07-10T17:57:20Z","timestamp":1657475840000},"page":"107019","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":21,"special_numbering":"C","title":["Enhancing classification of cells procured from bone marrow aspirate smears using generative adversarial networks and sequential convolutional neural network"],"prefix":"10.1016","volume":"224","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9660-4700","authenticated-orcid":false,"given":"Debapriya","family":"Hazra","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yung-Cheol","family":"Byun","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4999-1493","authenticated-orcid":false,"given":"Woo Jin","family":"Kim","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.cmpb.2022.107019_bib0001","article-title":"Generative adversarial nets","volume":"27","author":"Goodfellow","year":"2014","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.cmpb.2022.107019_bib0002","first-page":"1097","article-title":"Imagenet classification with deep convolutional neural networks","volume":"25","author":"Krizhevsky","year":"2012","journal-title":"Adv. Neural Inf. Process. Syst."},{"issue":"10","key":"10.1016\/j.cmpb.2022.107019_bib0003","doi-asserted-by":"crossref","first-page":"519","DOI":"10.3390\/sym10100519","article-title":"Synthetic medical images using f&bgan for improved lung nodules classification by multi-scale vgg16","volume":"10","author":"Zhao","year":"2018","journal-title":"Symmetry (Basel)"},{"key":"10.1016\/j.cmpb.2022.107019_bib0004","article-title":"High-resolution medical image synthesis using progressively grown generative adversarial networks","author":"Beers","year":"2018","journal-title":"arXiv preprint arXiv:1805.03144"},{"key":"10.1016\/j.cmpb.2022.107019_bib0005","unstructured":"W. Dai, X. Liang, H. Zhang, E. Xing, J. Doyle, Structure correcting adversarial network for chest x-rays organ segmentation, 2020, (????). US Patent 10,699,412."},{"key":"10.1016\/j.cmpb.2022.107019_bib0006","series-title":"International conference on information processing in medical imaging","first-page":"146","article-title":"Unsupervised anomaly detection with generative adversarial networks to guide marker discovery","author":"Schlegl","year":"2017"},{"issue":"8","key":"10.1016\/j.cmpb.2022.107019_bib0007","first-page":"1312","article-title":"Upsampling real-time, low-resolution cctv videos using generative adversarial networks","volume":"9","author":"Hazra","year":"2020","journal-title":"Electronics (Basel)"},{"issue":"15","key":"10.1016\/j.cmpb.2022.107019_bib0008","doi-asserted-by":"crossref","first-page":"3269","DOI":"10.3390\/s19153269","article-title":"A hyperspectral image classification method based on multi-discriminator generative adversarial networks","volume":"19","author":"Gao","year":"2019","journal-title":"Sensors"},{"key":"10.1016\/j.cmpb.2022.107019_bib0009","series-title":"2018 IEEE 15th international symposium on biomedical imaging (ISBI 2018)","first-page":"289","article-title":"Synthetic data augmentation using gan for improved liver lesion classification","author":"Frid-Adar","year":"2018"},{"key":"10.1016\/j.cmpb.2022.107019_bib0010","article-title":"Melanogans: high resolution skin lesion synthesis with gans","author":"Baur","year":"2018","journal-title":"arXiv preprint arXiv:1804.04338"},{"key":"10.1016\/j.cmpb.2022.107019_bib0011","article-title":"Skin lesion analysis toward melanoma detection 2018: a challenge hosted by the international skin imaging collaboration (isic)","author":"Codella","year":"2019","journal-title":"arXiv preprint arXiv:1902.03368"},{"key":"10.1016\/j.cmpb.2022.107019_bib0012","series-title":"2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)","first-page":"916","article-title":"Skin lesion classification using gan based data augmentation","author":"Rashid","year":"2019"},{"key":"10.1016\/j.cmpb.2022.107019_bib0013","doi-asserted-by":"crossref","first-page":"91916","DOI":"10.1109\/ACCESS.2020.2994762","article-title":"Covidgan: data augmentation using auxiliary classifier gan for improved covid-19 detection","volume":"8","author":"Waheed","year":"2020","journal-title":"IEEE Access"},{"key":"10.1016\/j.cmpb.2022.107019_bib0014","series-title":"2021 8th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI)","first-page":"203","article-title":"White blood cell subtype detection and classification","author":"Praveen","year":"2021"},{"issue":"1","key":"10.1016\/j.cmpb.2022.107019_bib0015","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41598-020-59215-9","article-title":"Efficient classification of white blood cell leukemia with improved swarm optimization of deep features","volume":"10","author":"Sahlol","year":"2020","journal-title":"Sci. Rep."},{"issue":"2","key":"10.1016\/j.cmpb.2022.107019_bib0016","doi-asserted-by":"crossref","first-page":"248","DOI":"10.3390\/diagnostics12020248","article-title":"An efficient multi-level convolutional neural network approach for white blood cells classification","volume":"12","author":"Cheuque","year":"2022","journal-title":"Diagnostics"},{"key":"10.1016\/j.cmpb.2022.107019_bib0017","unstructured":"W. Kim, [23] eonelab.co.kr, molecular diagnostic research center\u2019, 1983, 1983, (https:\/\/www.eonelab.co.kr\/global\/en\/main\/main.asp). Accessed October 15, 2021."},{"key":"10.1016\/j.cmpb.2022.107019_bib0018","article-title":"A single-cell morphological dataset of leukocytes from aml patients and non-malignant controls (aml-cytomorphology_lmu)","author":"Matek","year":"2019","journal-title":"The Cancer Imaging Archive (TCIA)[Internet].[cited 29 Oct 2019]. Available: https:\/\/wiki. cancerimagingarchive. net\/pages\/viewpage. action"},{"key":"10.1016\/j.cmpb.2022.107019_bib0019","doi-asserted-by":"crossref","DOI":"10.1016\/j.dib.2020.105474","article-title":"A dataset of microscopic peripheral blood cell images for development of automatic recognition systems","author":"Acevedo","year":"2020","journal-title":"Data in Brief, ISSN: 23523409, Vol. 30,(2020)"},{"key":"10.1016\/j.cmpb.2022.107019_bib0020","series-title":"International conference on machine learning","first-page":"214","article-title":"Wasserstein generative adversarial networks","author":"Arjovsky","year":"2017"},{"key":"10.1016\/j.cmpb.2022.107019_bib0021","article-title":"Improved training of wasserstein gans","author":"Gulrajani","year":"2017","journal-title":"arXiv preprint arXiv:1704.00028"},{"key":"10.1016\/j.cmpb.2022.107019_bib0022","article-title":"Conditional generative adversarial nets","author":"Mirza","year":"2014","journal-title":"arXiv preprint arXiv:1411.1784"},{"key":"10.1016\/j.cmpb.2022.107019_bib0023","series-title":"International conference on machine learning","first-page":"2642","article-title":"Conditional image synthesis with auxiliary classifier gans","author":"Odena","year":"2017"},{"key":"10.1016\/j.cmpb.2022.107019_bib0024","doi-asserted-by":"crossref","first-page":"1009","DOI":"10.1016\/j.ins.2019.10.014","article-title":"Conditional wasserstein generative adversarial network-gradient penalty-based approach to alleviating imbalanced data classification","volume":"512","author":"Zheng","year":"2020","journal-title":"Inf. Sci. (Ny)"},{"key":"10.1016\/j.cmpb.2022.107019_bib0025","series-title":"Proceedings of the 30th International Conference on Neural Information Processing Systems","first-page":"2180","article-title":"Infogan: Interpretable representation learning by information maximizing generative adversarial nets","author":"Chen","year":"2016"},{"key":"10.1016\/j.cmpb.2022.107019_bib0026","article-title":"Unsupervised representation learning with deep convolutional generative adversarial networks","author":"Radford","year":"2015","journal-title":"arXiv preprint arXiv:1511.06434"},{"key":"10.1016\/j.cmpb.2022.107019_bib0027","first-page":"1","article-title":"Transfer learning for image classification using vgg19: caltech-101 image data set","author":"Bansal","year":"2021","journal-title":"J. Ambient. Intell. Humaniz. Comput."},{"key":"10.1016\/j.cmpb.2022.107019_bib0028","series-title":"2019 4th International Conference on Electrical Information and Communication Technology (EICT)","first-page":"1","article-title":"Transfer learning based plant diseases detection using resnet50","author":"Mukti","year":"2019"},{"key":"10.1016\/j.cmpb.2022.107019_bib0029","series-title":"2017 2nd International Conference on Image, Vision and Computing (ICIVC)","first-page":"783","article-title":"Inception-v3 for flower classification","author":"Xia","year":"2017"},{"key":"10.1016\/j.cmpb.2022.107019_bib0030","series-title":"2020 2nd International Conference on Information Technology and Computer Application (ITCA)","first-page":"262","article-title":"An xception based convolutional neural network for scene image classification with transfer learning","author":"Wu","year":"2020"},{"key":"10.1016\/j.cmpb.2022.107019_bib0031","series-title":"International Conference on Machine Learning","first-page":"6105","article-title":"Efficientnet: Rethinking model scaling for convolutional neural networks","author":"Tan","year":"2019"},{"issue":"5\u20136","key":"10.1016\/j.cmpb.2022.107019_bib0032","doi-asserted-by":"crossref","first-page":"352","DOI":"10.1016\/S1532-0464(03)00034-0","article-title":"Logistic regression and artificial neural network classification models: a methodology review","volume":"35","author":"Dreiseitl","year":"2002","journal-title":"J. Biomed. Inform."},{"issue":"2","key":"10.1016\/j.cmpb.2022.107019_bib0033","doi-asserted-by":"crossref","first-page":"88","DOI":"10.18201\/ijisae.2019252786","article-title":"Performance analysis of ann and naive bayes classification algorithm for data classification","volume":"7","author":"Saritas","year":"2019","journal-title":"Int. J. Intell. Syst. Appl. Eng."},{"issue":"2","key":"10.1016\/j.cmpb.2022.107019_bib0034","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1109\/LGRS.2008.915597","article-title":"Multiclass and binary svm classification: implications for training and classification users","volume":"5","author":"Mathur","year":"2008","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"10.1016\/j.cmpb.2022.107019_bib0035","series-title":"2013 IEEE international conference on green computing and communications and IEEE Internet of Things and IEEE cyber, physical and social computing","first-page":"663","article-title":"Random forest classification for detecting android malware","author":"Alam","year":"2013"},{"key":"10.1016\/j.cmpb.2022.107019_bib0036","doi-asserted-by":"crossref","DOI":"10.34133\/2021\/9893804","article-title":"Label-free white blood cell classification using refractive index tomography and deep learning","volume":"2021","author":"Ryu","year":"2021","journal-title":"BME Front."},{"issue":"1168","key":"10.1016\/j.cmpb.2022.107019_bib0037","doi-asserted-by":"crossref","first-page":"1168","DOI":"10.12688\/f1000research.73315.1","article-title":"Wbc-based segmentation and classification on microscopic images: a minor improvement","volume":"10","author":"Lam","year":"2021","journal-title":"F1000Res"},{"issue":"1","key":"10.1016\/j.cmpb.2022.107019_bib0038","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41467-021-22518-0","article-title":"Democratising deep learning for microscopy with zerocostdl4mic","volume":"12","author":"von Chamier","year":"2021","journal-title":"Nat. Commun."},{"key":"10.1016\/j.cmpb.2022.107019_bib0039","doi-asserted-by":"crossref","DOI":"10.1145\/3502287","article-title":"A systematic review on data scarcity problem in deep learning: solution and applications","author":"Bansal","year":"2022","journal-title":"ACM Comput. Surv. (CSUR)"}],"container-title":["Computer Methods and Programs in Biomedicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0169260722004011?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0169260722004011?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,9,30]],"date-time":"2025-09-30T04:14:20Z","timestamp":1759205660000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0169260722004011"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,9]]},"references-count":39,"alternative-id":["S0169260722004011"],"URL":"https:\/\/doi.org\/10.1016\/j.cmpb.2022.107019","relation":{},"ISSN":["0169-2607"],"issn-type":[{"value":"0169-2607","type":"print"}],"subject":[],"published":{"date-parts":[[2022,9]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Enhancing classification of cells procured from bone marrow aspirate smears using generative adversarial networks and sequential convolutional neural network","name":"articletitle","label":"Article Title"},{"value":"Computer Methods and Programs in Biomedicine","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.cmpb.2022.107019","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2022 Elsevier B.V. All rights reserved.","name":"copyright","label":"Copyright"}],"article-number":"107019"}}