{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,11]],"date-time":"2026-04-11T16:23:44Z","timestamp":1775924624796,"version":"3.50.1"},"reference-count":53,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2023,11,21]],"date-time":"2023-11-21T00:00:00Z","timestamp":1700524800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,11,21]],"date-time":"2023-11-21T00:00:00Z","timestamp":1700524800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput & Applic"],"published-print":{"date-parts":[[2024,2]]},"DOI":"10.1007\/s00521-023-09157-w","type":"journal-article","created":{"date-parts":[[2023,11,21]],"date-time":"2023-11-21T16:02:45Z","timestamp":1700582565000},"page":"2507-2524","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["LeukoCapsNet: a resource-efficient modified CapsNet model to identify leukemia from blood smear images"],"prefix":"10.1007","volume":"36","author":[{"given":"Sabrina","family":"Dhalla","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3066-1956","authenticated-orcid":false,"given":"Ajay","family":"Mittal","sequence":"additional","affiliation":[]},{"given":"Savita","family":"Gupta","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,11,21]]},"reference":[{"key":"9157_CR1","unstructured":"WHO: https:\/\/gco.iarc.fr\/today\/data\/factsheets\/cancers (2020)"},{"issue":"7","key":"9157_CR2","doi-asserted-by":"publisher","first-page":"955","DOI":"10.1002\/cncr.28531","volume":"120","author":"JY-S Lim","year":"2014","unstructured":"Lim JY-S, Bhatia S, Robison LL, Yang JJ (2014) Genomics of racial and ethnic disparities in childhood acute lymphoblastic leukemia. Cancer 120(7):955\u2013962","journal-title":"Cancer"},{"issue":"2","key":"9157_CR3","doi-asserted-by":"publisher","first-page":"198","DOI":"10.1016\/S0031-3025(16)38671-8","volume":"10","author":"M Levine","year":"1978","unstructured":"Levine M (1978) Hematrak automated differential counter. Pathology 10(2):198","journal-title":"Pathology"},{"issue":"1","key":"9157_CR4","first-page":"3","volume":"17","author":"GK Megla","year":"1973","unstructured":"Megla GK (1973) The LARC automatic white blood cell analyzer. Acta Cytol 17(1):3\u201314","journal-title":"Acta Cytol"},{"key":"9157_CR5","unstructured":"Sabour S, Frosst N, Hinton GE (2017) Dynamic routing between capsules. In: Advances in neural information processing systems 30"},{"key":"9157_CR6","doi-asserted-by":"crossref","unstructured":"Afshar P, Mohammadi A, Plataniotis KN (2018) Brain tumor type classification via capsule networks. In: 2018 25th IEEE international conference on image processing (ICIP). IEEE, pp 3129\u20133133","DOI":"10.1109\/ICIP.2018.8451379"},{"key":"9157_CR7","doi-asserted-by":"crossref","unstructured":"Tian T, Liu X, Wang L (2019) Remote sensing scene classification based on res-capsnet. In: IGARSS 2019\u20132019 IEEE international geoscience and remote sensing symposium. IEEE, pp 525\u2013528","DOI":"10.1109\/IGARSS.2019.8898656"},{"issue":"11","key":"9157_CR8","first-page":"994","volume":"79","author":"SC Satapathy","year":"2020","unstructured":"Satapathy SC, Cruz M, Namburu A, Chakkaravarthy S, Pittendreigh M (2020) Skin cancer classification using convolutional capsule network (CAPSNET). J Sci Ind Res (JSIR) 79(11):994\u20131001","journal-title":"J Sci Ind Res (JSIR)"},{"key":"9157_CR9","doi-asserted-by":"crossref","unstructured":"Shi J, Guo S, Huo N, Zhang Y, Jin Q (2021) Sequence-to-sequence singing voice synthesis with perceptual entropy loss. In: ICASSP 2021-2021 IEEE international conference on acoustics, speech and signal processing (ICASSP). IEEE, pp 76\u201380","DOI":"10.1109\/ICASSP39728.2021.9414348"},{"key":"9157_CR10","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2020.107531","volume":"108","author":"X Yang","year":"2020","unstructured":"Yang X, Wu T, Wang N, Huang Y, Song B, Gao X (2020) HCNN-PSI: a hybrid CNN with partial semantic information for space target recognition. Pattern Recogn 108:107531","journal-title":"Pattern Recogn"},{"issue":"10","key":"9157_CR11","doi-asserted-by":"publisher","first-page":"11343","DOI":"10.1007\/s10489-021-02984-1","volume":"52","author":"C Xu","year":"2022","unstructured":"Xu C, Li Q, Zhou M, Zhou Q, Zhou Y, Ma Y (2022) RGB-T salient object detection via CNN feature and result saliency map fusion. Appl Intell 52(10):11343\u201311362","journal-title":"Appl Intell"},{"issue":"3","key":"9157_CR12","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuri.2021.100016","volume":"2","author":"SD Pande","year":"2022","unstructured":"Pande SD, Jadhav PP, Joshi R, Sawant AD, Muddebihalkar V, Rathod S, Gurav MN, Das S (2022) Digitization of handwritten devanagari text using CNN transfer learning\u2014a better customer service support. Neurosci Inform 2(3):100016","journal-title":"Neurosci Inform"},{"key":"9157_CR13","doi-asserted-by":"crossref","unstructured":"Parvaresh H, Sajedi H, Rahimi SA (2018) Leukemia diagnosis using image processing and computational intelligence. In: 2018 IEEE 22nd international conference on intelligent engineering systems (INES). IEEE, pp 000305\u2013000310","DOI":"10.1109\/INES.2018.8523900"},{"issue":"4","key":"9157_CR14","doi-asserted-by":"publisher","first-page":"120","DOI":"10.3390\/bioengineering7040120","volume":"7","author":"S Dasariraju","year":"2020","unstructured":"Dasariraju S, Huo M, McCalla S (2020) Detection and classification of immature leukocytes for diagnosis of acute myeloid leukemia using random forest algorithm. Bioengineering 7(4):120","journal-title":"Bioengineering"},{"issue":"6","key":"9157_CR15","doi-asserted-by":"publisher","first-page":"1045","DOI":"10.1007\/s10278-013-9622-7","volume":"26","author":"K Clark","year":"2013","unstructured":"Clark K, Vendt B, Smith K, Freymann J, Kirby J, Koppel P, Moore S, Phillips S, Maffitt D, Pringle M (2013) The cancer imaging archive (TCIA): maintaining and operating a public information repository. J Digit Imaging 26(6):1045\u20131057","journal-title":"J Digit Imaging"},{"issue":"06","key":"9157_CR16","doi-asserted-by":"publisher","first-page":"2150041","DOI":"10.1142\/S021951942150041X","volume":"21","author":"GM Bai","year":"2021","unstructured":"Bai GM, Venkadesh P (2021) Taylor-monarch butterfly optimization-based support vector machine for acute lymphoblastic leukemia classification with blood smear microscopic images. J Mech Med Biol 21(06):2150041","journal-title":"J Mech Med Biol"},{"key":"9157_CR17","doi-asserted-by":"crossref","unstructured":"Labati RD, Piuri V, Scotti F (2011) ALL-IDB: The acute lymphoblastic leukemia image database for image processing. In: 2011 18th IEEE international conference on image processing. IEEE, pp 2045\u20132048","DOI":"10.1109\/ICIP.2011.6115881"},{"key":"9157_CR18","doi-asserted-by":"crossref","unstructured":"Kuresan H, Sabastian\u00a0Satish J, Shanmugam N (2022) Analysis of blood cancer using microscopic image processing. In: Soft Computing and Signal Processing: Proceedings of 3rd ICSCSP 2020, vol 2, pp 403\u2013415. Springer, Berlin","DOI":"10.1007\/978-981-16-1249-7_38"},{"key":"9157_CR19","doi-asserted-by":"crossref","unstructured":"Rajpurohit S, Patil S, Choudhary N, Gavasane S, Kosamkar P (2018) Identification of acute lymphoblastic leukemia in microscopic blood image using image processing and machine learning algorithms. In: 2018 international conference on advances in computing, communications and informatics (ICACCI). IEEE, pp 2359\u20132363","DOI":"10.1109\/ICACCI.2018.8554576"},{"key":"9157_CR20","doi-asserted-by":"crossref","unstructured":"Ma K, Sun L, Wang Y, Wang J (2019) Classification of blood cancer images using a convolutional neural networks ensemble. In: Eleventh international conference on digital image processing (ICDIP 2019). International Society for Optics and Photonics, vol 11179, p 1117903","DOI":"10.1117\/12.2539605"},{"key":"9157_CR21","doi-asserted-by":"crossref","unstructured":"Dhalla S, Mittal A, Gupta S, Singh H (2021) Multi-model ensemble to classify acute lymphoblastic leukemia in blood smear images. In: International conference on pattern recognition. Springer, pp 243\u2013253","DOI":"10.1007\/978-3-030-68763-2_18"},{"key":"9157_CR22","doi-asserted-by":"publisher","DOI":"10.1155\/2021\/7529893","author":"Z Jiang","year":"2021","unstructured":"Jiang Z, Dong Z, Wang L, Jiang W (2021) Method for diagnosis of acute lymphoblastic leukemia based on VIT-CNN ensemble model. Comput Intell Neurosci. https:\/\/doi.org\/10.1155\/2021\/7529893","journal-title":"Comput Intell Neurosci"},{"issue":"5","key":"9157_CR23","first-page":"1","volume":"22","author":"Y-M Chen","year":"2021","unstructured":"Chen Y-M, Chou F-I, Ho W-H, Tsai J-T (2021) Classifying microscopic images as acute lymphoblastic leukemia by resnet ensemble model and Taguchi method. BMC Bioinform 22(5):1\u201321","journal-title":"BMC Bioinform"},{"key":"9157_CR24","doi-asserted-by":"crossref","unstructured":"Supardi N, Mashor M, Harun N, Bakri F, Hassan R (2012) Classification of blasts in acute leukemia blood samples using k-nearest neighbour. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications. IEEE, pp. 461\u2013465","DOI":"10.1109\/CSPA.2012.6194769"},{"key":"9157_CR25","doi-asserted-by":"crossref","unstructured":"Purwanti E, Calista E (2017) Detection of acute lymphocyte leukemia using k-nearest neighbor algorithm based on shape and histogram features. In: Journal of Physics: conference series. IOP Publishing, Bristol, vol 853, p 012011","DOI":"10.1088\/1742-6596\/853\/1\/012011"},{"issue":"3","key":"9157_CR26","first-page":"147","volume":"2","author":"MD Joshi","year":"2013","unstructured":"Joshi MD, Karode AH, Suralkar S (2013) White blood cells segmentation and classification to detect acute leukemia. Int J Emerg Trends Technol Comput Sci (IJETTCS) 2(3):147\u2013151","journal-title":"Int J Emerg Trends Technol Comput Sci (IJETTCS)"},{"key":"9157_CR27","doi-asserted-by":"crossref","unstructured":"Setiawan A, Harjoko A, Ratnaningsih T, Suryani E, Palgunadi S (2018) Classification of cell types in acute myeloid leukemia (AML) of m4, m5 and m7 subtypes with support vector machine classifier. In: 2018 international conference on information and communications technology (ICOIACT), pp 45\u201349. IEEE","DOI":"10.1109\/ICOIACT.2018.8350822"},{"issue":"1","key":"9157_CR28","doi-asserted-by":"publisher","first-page":"49","DOI":"10.4103\/2228-7477.150428","volume":"5","author":"MM Amin","year":"2015","unstructured":"Amin MM, Kermani S, Talebi A, Oghli MG (2015) Recognition of acute lymphoblastic leukemia cells in microscopic images using k-means clustering and support vector machine classifier. J Med Signals Sens 5(1):49","journal-title":"J Med Signals Sens"},{"issue":"18","key":"9157_CR29","doi-asserted-by":"publisher","first-page":"19057","DOI":"10.1007\/s11042-017-4478-3","volume":"76","author":"J Rawat","year":"2017","unstructured":"Rawat J, Singh A, Bhadauria H, Virmani J, Devgun JS (2017) Classification of acute lymphoblastic leukaemia using hybrid hierarchical classifiers. Multimedia Tools Appl 76(18):19057\u201319085","journal-title":"Multimedia Tools Appl"},{"issue":"3","key":"9157_CR30","doi-asserted-by":"publisher","first-page":"179","DOI":"10.1016\/j.artmed.2014.09.002","volume":"62","author":"L Putzu","year":"2014","unstructured":"Putzu L, Caocci G, Di Ruberto C (2014) Leucocyte classification for leukaemia detection using image processing techniques. Artif Intell Med 62(3):179\u2013191","journal-title":"Artif Intell Med"},{"issue":"10","key":"9157_CR31","doi-asserted-by":"publisher","first-page":"908","DOI":"10.1002\/jemt.22718","volume":"79","author":"M MoradiAmin","year":"2016","unstructured":"MoradiAmin M, Memari A, Samadzadehaghdam N, Kermani S, Talebi A (2016) Computer aided detection and classification of acute lymphoblastic leukemia cell subtypes based on microscopic image analysis. Microsc Res Tech 79(10):908\u2013916","journal-title":"Microsc Res Tech"},{"key":"9157_CR32","doi-asserted-by":"crossref","unstructured":"Vincent I, Kwon K-R, Lee S-H, Moon K-S (2015) Acute lymphoid leukemia classification using two-step neural network classifier. In: 2015 21st Korea\u2013Japan joint workshop on frontiers of computer vision (FCV). IEEE, pp 1\u20134","DOI":"10.1109\/FCV.2015.7103739"},{"key":"9157_CR33","doi-asserted-by":"crossref","unstructured":"Madhukar M, Agaian S, Chronopoulos AT (2012) New decision support tool for acute lymphoblastic leukemia classification. In: Image processing: algorithms and systems X; and parallel processing for imaging applications II. International society for optics and photonics, vol 8295, p 829518","DOI":"10.1117\/12.905969"},{"key":"9157_CR34","doi-asserted-by":"crossref","unstructured":"Mittal A, Dhalla S, Gupta S, Gupta A (2022) Automated analysis of blood smear images for leukemia detection: a comprehensive review. ACM Comput Surv (CSUR)","DOI":"10.1145\/3514495"},{"key":"9157_CR35","doi-asserted-by":"crossref","unstructured":"TTP T, Pham GN, Park J-H, Moon K-S, Lee S-H, Kwon K-R (2017) Acute leukemia classification using convolution neural network in clinical decision support system. In: CS &IT conference proceedings, vol 7. CS &IT conference proceedings","DOI":"10.5121\/csit.2017.71305"},{"key":"9157_CR36","unstructured":"Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In: Advances in neural information processing systems 25"},{"key":"9157_CR37","doi-asserted-by":"crossref","unstructured":"Ghosh A, Singh S, Sheet D (2017) Simultaneous localization and classification of acute lymphoblastic leukemic cells in peripheral blood smears using a deep convolutional network with average pooling layer. In: 2017 IEEE international conference on industrial and information systems (ICIIS). IEEE, pp. 1\u20136","DOI":"10.1109\/ICIINFS.2017.8300425"},{"key":"9157_CR38","doi-asserted-by":"publisher","unstructured":"Gupta A, Gupta R (2019) ISBI 2019 C-NMC challenge: classification in cancer cell imaging. Springer, Singapore. https:\/\/doi.org\/10.1007\/978-981-15-0798-4","DOI":"10.1007\/978-981-15-0798-4"},{"key":"9157_CR39","unstructured":"Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556"},{"key":"9157_CR40","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":"9157_CR41","doi-asserted-by":"crossref","unstructured":"Zhang X, Zhou X, Lin M, Sun J (2018) Shufflenet: an extremely efficient convolutional neural network for mobile devices. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 6848\u20136856","DOI":"10.1109\/CVPR.2018.00716"},{"key":"9157_CR42","doi-asserted-by":"crossref","unstructured":"Sandler M, Howard A, Zhu M, Zhmoginov A, Chen L-C (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510\u20134520","DOI":"10.1109\/CVPR.2018.00474"},{"key":"9157_CR43","doi-asserted-by":"crossref","unstructured":"Liu Y, Long F (2019) Acute lymphoblastic leukemia cells image analysis with deep bagging ensemble learning. In: ISBI 2019 C-NMC challenge: classification in cancer cell imaging: select proceedings, pp 113\u2013121 . Springer","DOI":"10.1007\/978-981-15-0798-4_12"},{"key":"9157_CR44","doi-asserted-by":"crossref","unstructured":"Szegedy C, Ioffe S, Vanhoucke V, Alemi AA (2017) Inception-v4, inception-resnet and the impact of residual connections on learning. In: Thirty-first AAAI conference on artificial intelligence","DOI":"10.1609\/aaai.v31i1.11231"},{"key":"9157_CR45","unstructured":"Mondal C, Hasan M, Jawad M, Dutta A, Islam M, Awal M, Ahmad M et al (2012) Acute lymphoblastic leukemia detection from microscopic images using weighted ensemble of convolutional neural networks. arXiv preprint arXiv:2105.03995"},{"key":"9157_CR46","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":"9157_CR47","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":"9157_CR48","unstructured":"Kingma DP, Ba J (2014) Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980"},{"issue":"1","key":"9157_CR49","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s40537-019-0197-0","volume":"6","author":"C Shorten","year":"2019","unstructured":"Shorten C, Khoshgoftaar TM (2019) A survey on image data augmentation for deep learning. J Big Data 6(1):1\u201348","journal-title":"J Big Data"},{"key":"9157_CR50","doi-asserted-by":"crossref","unstructured":"He K, Sun J (2015) Convolutional neural networks at constrained time cost. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 5353\u20135360","DOI":"10.1109\/CVPR.2015.7299173"},{"key":"9157_CR51","unstructured":"Srivastava RK, Greff K, Schmidhuber J (2015) Highway networks. arXiv preprint arXiv:1505.00387"},{"key":"9157_CR52","unstructured":"Li H, Kadav A, Durdanovic I, Samet H, Graf HP (2016) Pruning filters for efficient convnets. arXiv preprint arXiv:1608.08710"},{"key":"9157_CR53","doi-asserted-by":"crossref","unstructured":"Genovese A, Hosseini MS, Piuri V, Plataniotis KN, Scotti F (2021) Acute lymphoblastic leukemia detection based on adaptive unsharpening and deep learning. In: ICASSP 2021\u20132021 IEEE international conference on acoustics, speech and signal processing (ICASSP), pp 1205\u20131209. IEEE","DOI":"10.1109\/ICASSP39728.2021.9414362"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-023-09157-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-023-09157-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-023-09157-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,19]],"date-time":"2024-01-19T15:09:59Z","timestamp":1705676999000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-023-09157-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,21]]},"references-count":53,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2024,2]]}},"alternative-id":["9157"],"URL":"https:\/\/doi.org\/10.1007\/s00521-023-09157-w","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,11,21]]},"assertion":[{"value":"1 December 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 October 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 November 2023","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 that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}