{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,7]],"date-time":"2026-05-07T15:41:26Z","timestamp":1778168486862,"version":"3.51.4"},"reference-count":33,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2021,8,23]],"date-time":"2021-08-23T00:00:00Z","timestamp":1629676800000},"content-version":"vor","delay-in-days":234,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["81827807"],"award-info":[{"award-number":["81827807"]}],"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":["61675134"],"award-info":[{"award-number":["61675134"]}],"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":["61307015"],"award-info":[{"award-number":["61307015"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003399","name":"Science and Technology Commission of Shanghai Municipality","doi-asserted-by":"publisher","award":["19441905800"],"award-info":[{"award-number":["19441905800"]}],"id":[{"id":"10.13039\/501100003399","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Computational Intelligence and Neuroscience"],"published-print":{"date-parts":[[2021,1]]},"abstract":"<jats:p>Acute lymphocytic leukemia (ALL) is a deadly cancer that not only affects adults but also accounts for about 25% of childhood cancers. Timely and accurate diagnosis of the cancer is an important premise for effective treatment to improve survival rate. Since the image of leukemic B\u2010lymphoblast cells (cancer cells) under the microscope is very similar in morphology to that of normal B\u2010lymphoid precursors (normal cells), it is difficult to distinguish between cancer cells and normal cells. Therefore, we propose the ViT\u2010CNN ensemble model to classify cancer cells images and normal cells images to assist in the diagnosis of acute lymphoblastic leukemia. The ViT\u2010CNN ensemble model is an ensemble model that combines the vision transformer model and convolutional neural network (CNN) model. The vision transformer model is an image classification model based entirely on the transformer structure, which has completely different feature extraction method from the CNN model. The ViT\u2010CNN ensemble model can extract the features of cells images in two completely different ways to achieve better classification results. In addition, the data set used in this article is an unbalanced data set and has a certain amount of noise, and we propose a difference enhancement\u2010random sampling (DERS) data enhancement method, create a new balanced data set, and use the symmetric cross\u2010entropy loss function to reduce the impact of noise in the data set. The classification accuracy of the ViT\u2010CNN ensemble model on the test set has reached 99.03%, and it is proved through experimental comparison that the effect is better than other models. The proposed method can accurately distinguish between cancer cells and normal cells and can be used as an effective method for computer\u2010aided diagnosis of acute lymphoblastic leukemia.<\/jats:p>","DOI":"10.1155\/2021\/7529893","type":"journal-article","created":{"date-parts":[[2021,8,23]],"date-time":"2021-08-23T18:20:25Z","timestamp":1629742825000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":102,"title":["Method for Diagnosis of Acute Lymphoblastic Leukemia Based on ViT\u2010CNN Ensemble Model"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6066-4875","authenticated-orcid":false,"given":"Zhencun","family":"Jiang","sequence":"first","affiliation":[]},{"given":"Zhengxin","family":"Dong","sequence":"additional","affiliation":[]},{"given":"Lingyang","family":"Wang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3548-0994","authenticated-orcid":false,"given":"Wenping","family":"Jiang","sequence":"additional","affiliation":[]}],"member":"311","published-online":{"date-parts":[[2021,8,23]]},"reference":[{"key":"e_1_2_9_1_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11033-020-06073-3"},{"key":"e_1_2_9_2_2","doi-asserted-by":"publisher","DOI":"10.1186\/s40164-020-00189-9"},{"key":"e_1_2_9_3_2","first-page":"84","article-title":"Application of cell morphology-related technology in hematological tumors","volume":"39","author":"Peng X.","year":"2019","journal-title":"Chinese Journal of Biological Engineering"},{"key":"e_1_2_9_4_2","doi-asserted-by":"publisher","DOI":"10.1155\/2021\/5592878"},{"key":"e_1_2_9_5_2","doi-asserted-by":"publisher","DOI":"10.1155\/2019\/8162567"},{"key":"e_1_2_9_6_2","doi-asserted-by":"publisher","DOI":"10.3390\/s20123482"},{"key":"e_1_2_9_7_2","doi-asserted-by":"publisher","DOI":"10.3390\/s20236838"},{"key":"e_1_2_9_8_2","doi-asserted-by":"publisher","DOI":"10.1002\/rcs.2194"},{"key":"e_1_2_9_9_2","doi-asserted-by":"publisher","DOI":"10.3390\/diagnostics9030104"},{"key":"e_1_2_9_10_2","doi-asserted-by":"publisher","DOI":"10.1136\/jclinpath-2019-205949"},{"key":"e_1_2_9_11_2","doi-asserted-by":"publisher","DOI":"10.3390\/diagnostics10121064"},{"key":"e_1_2_9_12_2","doi-asserted-by":"publisher","DOI":"10.3390\/s21020455"},{"key":"e_1_2_9_13_2","doi-asserted-by":"publisher","DOI":"10.3390\/s20061753"},{"key":"e_1_2_9_14_2","doi-asserted-by":"publisher","DOI":"10.3390\/s20236713"},{"key":"e_1_2_9_15_2","doi-asserted-by":"publisher","DOI":"10.3390\/rs13030516"},{"key":"e_1_2_9_16_2","doi-asserted-by":"crossref","unstructured":"GuptaR. MallickP. DuggalR. GuptaA. andSharmaO. Stain color normalization and segmentation of plasma cells in microscopic images as a prelude to development of computer assisted automated disease diagnostic tool in multiple myeloma Proceedings of the 16th International Myeloma Workshop (IMW) March 2017 New Delhi India https:\/\/doi.org\/10.1016\/j.clml.2017.03.178.","DOI":"10.1016\/j.clml.2017.03.178"},{"key":"e_1_2_9_17_2","doi-asserted-by":"crossref","unstructured":"DuggalR. GuptaA. GuptaR. WadhwaM. andAhujaC. Overlapping cell nuclei segmentation in microscopic images using deep belief networks Proceedings of the Indian Conference on Computer Vision Graphics and Image Processing (ICVGIP) December 2016 Guwahati India.","DOI":"10.1145\/3009977.3010043"},{"key":"e_1_2_9_18_2","unstructured":"DuggalR. GuptaA. andGuptaR. Segmentation of overlapping\/touching white blood cell nuclei using artificial neural networks Proceedings of the CME Series on Hemato-Oncopathology July 2016 New Delhi India All India Institute of Medical Sciences (AIIMS)."},{"key":"e_1_2_9_19_2","doi-asserted-by":"crossref","unstructured":"DuggalR. GuptaA. GuptaR. andMallickP. DescoteauxM. Maier-HeinL. FranzA. JanninP. CollinsD. andDuchesneS. SD-layer: stain deconvolutional layer for CNNs in medical microscopic imaging Proceedings of the Medical Image Computing and Computer-Assisted Intervention\u2212MICCAI 2017 September 2017 Quebec City Canada Springer 435\u2013443 https:\/\/doi.org\/10.1007\/978-3-319-66179-7_50 2-s2.0-85029513084.","DOI":"10.1007\/978-3-319-66179-7_50"},{"key":"e_1_2_9_20_2","doi-asserted-by":"publisher","DOI":"10.3390\/info11020125"},{"key":"e_1_2_9_21_2","doi-asserted-by":"publisher","DOI":"10.1155\/2020\/8864698"},{"key":"e_1_2_9_22_2","unstructured":"DosovitskiyA. BeyerL. KolesnikovA.et al. An image is worth 16 \u00d7 16 words: transformers for image recognition at scale 2020 http:\/\/arxiv.org\/abs\/2010.11929."},{"key":"e_1_2_9_23_2","doi-asserted-by":"publisher","DOI":"10.1155\/2019\/6212759"},{"key":"e_1_2_9_24_2","doi-asserted-by":"publisher","DOI":"10.1155\/2020\/8826568"},{"key":"e_1_2_9_25_2","unstructured":"TanM.andLeQ. Efficientnet: rethinking model scaling for convolutional neural networks Proceedings of the International Conference on Machine Learning June 2019 Long Beach CA USA 6105\u20136114."},{"key":"e_1_2_9_26_2","doi-asserted-by":"publisher","DOI":"10.1155\/2021\/8890226"},{"key":"e_1_2_9_27_2","doi-asserted-by":"publisher","DOI":"10.1155\/2016\/6212684"},{"key":"e_1_2_9_28_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2021.106184"},{"key":"e_1_2_9_29_2","article-title":"Research on fire detection of improved VGG16 image recognition based on deep learning","volume":"40","author":"Zhen-Cun J.","year":"2021","journal-title":"Fire Science and Technology"},{"key":"e_1_2_9_30_2","first-page":"109","article-title":"Research on early fire detection of Yolo V5 based on multiple transfer learning","volume":"40","author":"Wen-Ping J.","year":"2021","journal-title":"Fire Science and Technology"},{"key":"e_1_2_9_31_2","doi-asserted-by":"crossref","unstructured":"WangY. MaX. ChenZ.et al. Symmetric cross entropy for robust learning with noisy labels Proceedings of the IEEE\/CVF International Conference on Computer Vision 2019 Montreal Canada 322\u2013330 https:\/\/doi.org\/10.1109\/iccv.2019.00041.","DOI":"10.1109\/ICCV.2019.00041"},{"key":"e_1_2_9_32_2","unstructured":"LiuL. JiangH. HeP.et al. On the variance of the adaptive learning rate and beyond 2019 http:\/\/arxiv.org\/abs\/1908.03265."},{"key":"e_1_2_9_33_2","unstructured":"PaszkeA. GrossS. MassaF.et al. Pytorch: an imperative style high-performance deep learning library 2019 http:\/\/arxiv.org\/abs\/912.01703."}],"container-title":["Computational Intelligence and Neuroscience"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/downloads.hindawi.com\/journals\/cin\/2021\/7529893.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/cin\/2021\/7529893.xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1155\/2021\/7529893","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,6]],"date-time":"2024-08-06T10:45:30Z","timestamp":1722941130000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1155\/2021\/7529893"}},"subtitle":[],"editor":[{"given":"Suresh","family":"Manic","sequence":"additional","affiliation":[]}],"short-title":[],"issued":{"date-parts":[[2021,1]]},"references-count":33,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2021,1]]}},"alternative-id":["10.1155\/2021\/7529893"],"URL":"https:\/\/doi.org\/10.1155\/2021\/7529893","archive":["Portico"],"relation":{},"ISSN":["1687-5265","1687-5273"],"issn-type":[{"value":"1687-5265","type":"print"},{"value":"1687-5273","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,1]]},"assertion":[{"value":"2021-05-26","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2021-08-07","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2021-08-23","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"7529893"}}