{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,5]],"date-time":"2026-05-05T07:29:18Z","timestamp":1777966158448,"version":"3.51.4"},"reference-count":38,"publisher":"Springer Science and Business Media LLC","issue":"17","license":[{"start":{"date-parts":[[2024,8,7]],"date-time":"2024-08-07T00:00:00Z","timestamp":1722988800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,8,7]],"date-time":"2024-08-07T00:00:00Z","timestamp":1722988800000},"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":["J Supercomput"],"published-print":{"date-parts":[[2024,11]]},"DOI":"10.1007\/s11227-024-06405-1","type":"journal-article","created":{"date-parts":[[2024,8,7]],"date-time":"2024-08-07T10:07:19Z","timestamp":1723025239000},"page":"25155-25187","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Efficient white blood cell identification with hybrid inception-xception network"],"prefix":"10.1007","volume":"80","author":[{"given":"Radhwan A. A.","family":"Saleh","sequence":"first","affiliation":[]},{"given":"Mustafa","family":"Ghaleb","sequence":"additional","affiliation":[]},{"given":"Wasswa","family":"Shafik","sequence":"additional","affiliation":[]},{"given":"H. Metin","family":"ERTUN\u00c7","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,8,7]]},"reference":[{"key":"6405_CR1","doi-asserted-by":"publisher","first-page":"105020","DOI":"10.1016\/j.cmpb.2019.105020","volume":"180","author":"A Acevedo","year":"2019","unstructured":"Acevedo A, Alf\u00e9rez S, Merino A, Puigv\u00ed L, Rodellar J (2019) Recognition of peripheral blood cell images using convolutional neural networks. Comput Method Program Biomed 180:105020","journal-title":"Comput Method Program Biomed"},{"key":"6405_CR2","doi-asserted-by":"publisher","first-page":"105474","DOI":"10.1016\/j.dib.2020.105474","volume":"30","author":"A Acevedo","year":"2020","unstructured":"Acevedo A, Merino A, Alf\u00e9rez S, Molina \u00c1, Bold\u00fa L, Rodellar J (2020) A dataset of microscopic peripheral blood cell images for development of automatic recognition systems. Data Brief 30:105474","journal-title":"Data Brief"},{"issue":"3","key":"6405_CR3","doi-asserted-by":"publisher","first-page":"352","DOI":"10.3390\/diagnostics13030352","volume":"13","author":"R Ahmad","year":"2023","unstructured":"Ahmad R, Awais M, Kausar N, Akram T (2023) White blood cells classification using entropy-controlled deep features optimization. Diagnostics 13(3):352","journal-title":"Diagnostics"},{"key":"6405_CR4","first-page":"44","volume":"18","author":"MMM Al-Hatab","year":"2023","unstructured":"Al-Hatab MMM, AlNima MZS et al (2023) Hematological classification of white blood cells by exploiting digital microscopic images. Eur Res Bull 18:44\u201352","journal-title":"Eur Res Bull"},{"issue":"2","key":"6405_CR5","doi-asserted-by":"publisher","first-page":"048","DOI":"10.30574\/wjbphs.2024.17.2.0052","volume":"17","author":"AAH Alrfooh","year":"2024","unstructured":"Alrfooh AAH, Obeidat HSM, Sbaihat ASQ, El Omari ZA, Romman MZFA (2024) Corticosteroidal impacts on white blood cells differentials in ear-nose-throat admitted infected patients. World J Biol Pharm Health Sci 17(2):048\u2013058","journal-title":"World J Biol Pharm Health Sci"},{"key":"6405_CR6","doi-asserted-by":"crossref","unstructured":"Balasubramanian K, Ananthamoorthy N, Ramya K (2022) An approach to classify white blood cells using convolutional neural network optimized by particle swarm optimization algorithm. Neural Computing and Applications pp. 1\u201313","DOI":"10.1007\/s00521-022-07279-1"},{"key":"6405_CR7","doi-asserted-by":"crossref","unstructured":"Basu A, Senapati P, Deb M, Rai R, Dhal KG (2023) A survey on recent trends in deep learning for nucleus segmentation from histopathology images. Evolving Systems pp. 1\u201346","DOI":"10.1007\/s12530-023-09491-3"},{"key":"6405_CR8","doi-asserted-by":"publisher","first-page":"101699","DOI":"10.1016\/j.compmedimag.2020.101699","volume":"80","author":"YY Baydilli","year":"2020","unstructured":"Baydilli YY, Atila \u00dc (2020) Classification of white blood cells using capsule networks. Comput Med Imag Graph 80:101699","journal-title":"Comput Med Imag Graph"},{"key":"6405_CR9","doi-asserted-by":"publisher","first-page":"108913","DOI":"10.1016\/j.compeleceng.2023.108913","volume":"110","author":"K Bhatia","year":"2023","unstructured":"Bhatia K, Dhalla S, Mittal A, Gupta S, Gupta A, Jindal A (2023) Integrating explainability into deep learning-based models for white blood cells classification. Comput Electr Eng 110:108913","journal-title":"Comput Electr Eng"},{"issue":"3","key":"6405_CR10","doi-asserted-by":"publisher","first-page":"e0296701","DOI":"10.1371\/journal.pone.0296701","volume":"19","author":"Y Bi","year":"2024","unstructured":"Bi Y, Gao Y, Xie Y, Zhou M, Liu Z, Tian S, Sun C (2024) White blood cells and type 2 diabetes: a mendelian randomization study. Plos one 19(3):e0296701","journal-title":"Plos one"},{"key":"6405_CR11","doi-asserted-by":"publisher","first-page":"105265","DOI":"10.1016\/j.compbiomed.2022.105265","volume":"143","author":"H Chen","year":"2022","unstructured":"Chen H, Li C, Li X, Rahaman MM, Hu W, Li Y, Liu W, Sun C, Sun H, Huang X et al (2022) Il-mcam: an interactive learning and multi-channel attention mechanism-based weakly supervised colorectal histopathology image classification approach. Comput Biol Med 143:105265","journal-title":"Comput Biol Med"},{"key":"6405_CR12","doi-asserted-by":"publisher","DOI":"10.3390\/diagnostics12020248","author":"C Cheuque","year":"2022","unstructured":"Cheuque C, Querales M, Le\u00f3n R, Salas R, Torres R (2022) An efficient multi-level convolutional neural network approach for white blood cells classification. Diagnostics. https:\/\/doi.org\/10.3390\/diagnostics12020248","journal-title":"Diagnostics"},{"issue":"1","key":"6405_CR13","doi-asserted-by":"publisher","first-page":"249","DOI":"10.1007\/s11227-023-05490-y","volume":"80","author":"N Dong","year":"2023","unstructured":"Dong N, Feng Q, Chang J, Mai X (2023) White blood cell classification based on a novel ensemble convolutional neural network framework. J Supercomput 80(1):249\u2013270","journal-title":"J Supercomput"},{"issue":"2","key":"6405_CR14","doi-asserted-by":"publisher","first-page":"196","DOI":"10.3390\/diagnostics13020196","volume":"13","author":"TA Elhassan","year":"2023","unstructured":"Elhassan TA, Mohd Rahim MS, Siti Zaiton MH, Swee TT, Alhaj TA, Ali A, Aljurf M (2023) Classification of atypical white blood cells in acute myeloid leukemia using a two-stage hybrid model based on deep convolutional autoencoder and deep convolutional neural network. Diagnostics 13(2):196","journal-title":"Diagnostics"},{"key":"6405_CR15","doi-asserted-by":"publisher","first-page":"108710","DOI":"10.1016\/j.compeleceng.2023.108710","volume":"108","author":"FIF Escobar","year":"2023","unstructured":"Escobar FIF, Alipo-on JRT, Novia JLU, Tan MJT, Karim HA, AlDahoul N (2023) Automated counting of white blood cells in thin blood smear images. Comput Electr Eng 108:108710","journal-title":"Comput Electr Eng"},{"issue":"16","key":"6405_CR16","doi-asserted-by":"publisher","first-page":"1646","DOI":"10.1182\/blood.2023020232","volume":"143","author":"AT Gerds","year":"2024","unstructured":"Gerds AT, Mesa R, Burke JM, Grunwald MR, Stein BL, Squier P, Yu J, Hamer-Maansson J, Oh ST (2024) Association between elevated white blood cell counts and thrombotic events in polycythemia vera: analysis from reveal. Blood 143(16):1646\u20131655","journal-title":"Blood"},{"key":"6405_CR17","doi-asserted-by":"crossref","unstructured":"Gill KS, Anand V, Gupta S, Figat P (2022) Stratification of white blood cells using optimized densenet201 model. In: International Conference on Advanced Communication and Intelligent Systems, pp. 31\u201347. Springer","DOI":"10.1007\/978-3-031-25088-0_3"},{"key":"6405_CR18","doi-asserted-by":"publisher","first-page":"103156","DOI":"10.1016\/j.bspc.2021.103156","volume":"71","author":"A Girdhar","year":"2022","unstructured":"Girdhar A, Kapur H, Kumar V (2022) Classification of white blood cell using convolution neural network. Biomed Sig Process Control 71:103156","journal-title":"Biomed Sig Process Control"},{"issue":"1","key":"6405_CR19","doi-asserted-by":"publisher","first-page":"37","DOI":"10.24138\/jcomss.v16i1.818","volume":"16","author":"H Mohamed","year":"2020","unstructured":"Mohamed H, ElBehaidy WH, Khoriba G, Li J (2020) Improved white blood cells classification based on pre-trained deep learning models. J Commun Software Syst 16(1):37\u201345","journal-title":"J Commun Software Syst"},{"key":"6405_CR20","doi-asserted-by":"publisher","first-page":"103611","DOI":"10.1016\/j.bspc.2022.103611","volume":"75","author":"Y Ha","year":"2022","unstructured":"Ha Y, Du Z, Tian J (2022) Fine-grained interactive attention learning for semi-supervised white blood cell classification. Biomed Sig Process Control 75:103611. https:\/\/doi.org\/10.1016\/j.bspc.2022.103611","journal-title":"Biomed Sig Process Control"},{"key":"6405_CR21","doi-asserted-by":"crossref","unstructured":"Habibzadeh M, Krzy\u017cak A, Fevens T (2013) White blood cell differential counts using convolutional neural networks for low resolution images. In: International Conference on Artificial Intelligence and Soft Computing, pp. 263\u2013274. Springer","DOI":"10.1007\/978-3-642-38610-7_25"},{"issue":"2","key":"6405_CR22","doi-asserted-by":"publisher","first-page":"627","DOI":"10.1007\/s13246-019-00742-9","volume":"42","author":"RB Hegde","year":"2019","unstructured":"Hegde RB, Prasad K, Hebbar H, Singh BMK (2019) Feature extraction using traditional image processing and convolutional neural network methods to classify white blood cells: a study. Aus Phys Eng Sci Med 42(2):627\u2013638. https:\/\/doi.org\/10.1007\/s13246-019-00742-9","journal-title":"Aus Phys Eng Sci Med"},{"key":"6405_CR23","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.thromres.2023.12.005","volume":"234","author":"D Jabrah","year":"2024","unstructured":"Jabrah D, Rossi R, Molina S, Douglas A, Pandit A, McCarthy R, Gilvarry M, Ceder E, Fitzgerald S, Dunker D et al (2024) White blood cell subtypes and neutrophil extracellular traps content as biomarkers for stroke etiology in acute ischemic stroke clots retrieved by mechanical thrombectomy. Thrombos Res 234:1\u20138","journal-title":"Thrombos Res"},{"key":"6405_CR24","doi-asserted-by":"publisher","first-page":"109472","DOI":"10.1016\/j.mehy.2019.109472","volume":"135","author":"H Kutlu","year":"2020","unstructured":"Kutlu H, Avci E, \u00d6zyurt F (2020) White blood cells detection and classification based on regional convolutional neural networks. Med Hypothese 135:109472. https:\/\/doi.org\/10.1016\/j.mehy.2019.109472","journal-title":"Med Hypothese"},{"issue":"10","key":"6405_CR25","doi-asserted-by":"publisher","first-page":"e37331","DOI":"10.1097\/MD.0000000000037331","volume":"103","author":"A Mahmood","year":"2024","unstructured":"Mahmood A, Haider H, Samad S, Kumar D, Perwaiz A, Mushtaq R, Ali A, Farooq MZ, Farhat H (2024) Association of white blood cell parameters with metabolic syndrome: a systematic review and meta-analysis of 168,000 patients. Medicine 103(10):e37331","journal-title":"Medicine"},{"issue":"2","key":"6405_CR26","doi-asserted-by":"publisher","first-page":"515","DOI":"10.1038\/s41440-023-01472-y","volume":"47","author":"A Mansoori","year":"2024","unstructured":"Mansoori A, Farizani Gohari NS, Etemad L, Poudineh M, Ahari RK, Mohammadyari F, Azami M, Rad ES, Ferns G, Esmaily H et al (2024) White blood cell and platelet distribution widths are associated with hypertension: data mining approaches. Hypertens Res 47(2):515\u2013528","journal-title":"Hypertens Res"},{"issue":"4","key":"6405_CR27","doi-asserted-by":"publisher","first-page":"947","DOI":"10.29207\/resti.v7i4.5182","volume":"7","author":"RF Mulya","year":"2023","unstructured":"Mulya RF, Utami E, Ariatmanto D et al (2023) Classification of acute lymphoblastic leukemia based on white blood cell images using inceptionv3 model. J RESTI (Rekayasa Sistem dan Teknologi Informasi) 7(4):947\u2013952","journal-title":"J RESTI (Rekayasa Sistem dan Teknologi Informasi)"},{"issue":"5","key":"6405_CR28","doi-asserted-by":"publisher","first-page":"378","DOI":"10.1016\/j.irbm.2020.08.005","volume":"42","author":"A Patil","year":"2021","unstructured":"Patil A, Patil M, Birajdar G (2021) White blood cells image classification using deep learning with canonical correlation analysis. Irbm 42(5):378\u2013389","journal-title":"Irbm"},{"issue":"1","key":"6405_CR29","doi-asserted-by":"publisher","first-page":"177","DOI":"10.1016\/j.numecd.2022.10.008","volume":"33","author":"D Peng","year":"2023","unstructured":"Peng D, Huang Y, Wang Y, Huang Z, Zhu Y, Shi R, Sun Y, Qin Y, Cao Y, Zhang X (2023) Combined prognostic significance of white blood cell count and d-dimer on in-hospital outcomes of acute ischemic stroke. Nutr Metab Cardiovasc Dis 33(1):177\u2013184","journal-title":"Nutr Metab Cardiovasc Dis"},{"key":"6405_CR30","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2022.105236","author":"P Rastogi","year":"2022","unstructured":"Rastogi P, Khanna K, Singh V (2022) Leufeatx: deep learning-based feature extractor for the diagnosis of acute leukemia from microscopic images of peripheral blood smear. Comput Biol Med. https:\/\/doi.org\/10.1016\/j.compbiomed.2022.105236","journal-title":"Comput Biol Med"},{"issue":"4","key":"6405_CR31","doi-asserted-by":"publisher","first-page":"333","DOI":"10.1016\/j.compmedimag.2011.01.003","volume":"35","author":"SH Rezatofighi","year":"2011","unstructured":"Rezatofighi SH, Soltanian-Zadeh H (2011) Automatic recognition of five types of white blood cells in peripheral blood. Comput Med Imag Graph 35(4):333\u2013343","journal-title":"Comput Med Imag Graph"},{"key":"6405_CR32","doi-asserted-by":"publisher","first-page":"695","DOI":"10.1007\/s40747-021-00473-z","volume":"115","author":"S Saba","year":"2022","unstructured":"Saba S, Javeria A, Muhammad S, Muhammad I, Shui-Hua W (2022) A deep network designed for segmentation and classification of leukemia using fusion of the transfer learning models. Complex Intell Syst 115:695\u2013708. https:\/\/doi.org\/10.1007\/s40747-021-00473-z","journal-title":"Complex Intell Syst"},{"key":"6405_CR33","doi-asserted-by":"crossref","unstructured":"Sharma M, Bhave A, Janghel RR (2019) White blood cell classification using convolutional neural network. In: Soft Computing and Signal Processing, pp. 135\u2013143. Springer","DOI":"10.1007\/978-981-13-3600-3_13"},{"key":"6405_CR34","doi-asserted-by":"crossref","unstructured":"Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A (2015) Going deeper with convolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 1\u20139","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"6405_CR35","doi-asserted-by":"publisher","first-page":"107875","DOI":"10.1016\/j.compbiomed.2023.107875","volume":"169","author":"SA Tarimo","year":"2024","unstructured":"Tarimo SA, Jang MA, Ngasa EE, Shin HB, Shin H, Woo J (2024) Wbc yolo-vit: 2 way-2 stage white blood cell detection and classification with a combination of yolov5 and vision transformer. Comput Biol Med 169:107875","journal-title":"Comput Biol Med"},{"issue":"8","key":"6405_CR36","doi-asserted-by":"publisher","first-page":"8625","DOI":"10.1007\/s12652-021-03289-4","volume":"12","author":"A Tuncer","year":"2021","unstructured":"Tuncer A (2021) Cost-optimized hybrid convolutional neural networks for detection of plant leaf diseases. J Ambient Intell Human Comput 12(8):8625\u20138636","journal-title":"J Ambient Intell Human Comput"},{"issue":"1","key":"6405_CR37","doi-asserted-by":"publisher","first-page":"159","DOI":"10.2174\/1570159X21666230104090046","volume":"22","author":"Y Zhang","year":"2024","unstructured":"Zhang Y, Tao S, Coid J, Wei W, Wang Q, Yue W, Yan H, Tan L, Chen Q, Yang G et al (2024) The role of total white blood cell count in antipsychotic treatment for patients with schizophrenia. Curr Neuropharmacol 22(1):159\u2013167","journal-title":"Curr Neuropharmacol"},{"key":"6405_CR38","doi-asserted-by":"crossref","unstructured":"Zhu D, Wang G (2024) Lafssd: lightweight and advanced fssd for multi-scale detection of platelets and white blood cells in human peripheral blood smear images. Multimedia Tools and Applications pp. 1\u201322","DOI":"10.1007\/s11042-024-18282-0"}],"container-title":["The Journal of Supercomputing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-024-06405-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11227-024-06405-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-024-06405-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,5]],"date-time":"2024-09-05T15:31:43Z","timestamp":1725550303000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11227-024-06405-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8,7]]},"references-count":38,"journal-issue":{"issue":"17","published-print":{"date-parts":[[2024,11]]}},"alternative-id":["6405"],"URL":"https:\/\/doi.org\/10.1007\/s11227-024-06405-1","relation":{},"ISSN":["0920-8542","1573-0484"],"issn-type":[{"value":"0920-8542","type":"print"},{"value":"1573-0484","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,8,7]]},"assertion":[{"value":"30 July 2024","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 August 2024","order":2,"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"}},{"value":"The manuscript is conducted in the ethical manner advised by the targeted journal.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"Not applicable.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The research is scientifically consented to be published.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}}]}}