{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T18:48:37Z","timestamp":1780339717143,"version":"3.54.1"},"reference-count":21,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2021,11,11]],"date-time":"2021-11-11T00:00:00Z","timestamp":1636588800000},"content-version":"vor","delay-in-days":314,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Hamadan University of Medical Sciences Research Council","award":["140002281448"],"award-info":[{"award-number":["140002281448"]}]}],"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><jats:italic>Background<\/jats:italic>. Leukemia is fatal cancer in both children and adults and is divided into acute and chronic. Acute lymphoblastic leukemia (ALL) is a subtype of this cancer. Early diagnosis of this disease can have a significant impact on the treatment of this disease. Computational intelligence\u2010oriented techniques can be used to help physicians identify and classify ALL rapidly. <jats:italic>Materials and Method<\/jats:italic>. In this study, the utilized dataset was collected from a CodaLab competition to classify leukemic cells from normal cells in microscopic images. Two famous deep learning networks, including residual neural network (ResNet\u201050) and VGG\u201016 were employed. These two networks are already trained by our assigned parameters, meaning we did not use the stored weights; we adjusted the weights and learning parameters too. Also, a convolutional network with ten convolutional layers and 2\u22172 max\u2010pooling layers\u2014with strides 2\u2014was proposed, and six common machine learning techniques were developed to classify acute lymphoblastic leukemia into two classes. <jats:italic>Results<\/jats:italic>. The validation accuracies (the mean accuracy of training and test networks for 100 training cycles) of the ResNet\u201050, VGG\u201016, and the proposed convolutional network were found to be 81.63%, 84.62%, and 82.10%, respectively. Among applied machine learning methods, the lowest obtained accuracy was related to multilayer perceptron (27.33%) and highest for random forest (81.72%). <jats:italic>Conclusion<\/jats:italic>. This study showed that the proposed convolutional neural network has optimal accuracy in the diagnosis of ALL. By comparing various convolutional neural networks and machine learning methods in diagnosing this disease, the convolutional neural network achieved good performance and optimal execution time without latency. This proposed network is less complex than the two pretrained networks and can be employed by pathologists and physicians in clinical systems for leukemia diagnosis.<\/jats:p>","DOI":"10.1155\/2021\/5478157","type":"journal-article","created":{"date-parts":[[2021,11,11]],"date-time":"2021-11-11T23:50:06Z","timestamp":1636674606000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":85,"title":["Timely Diagnosis of Acute Lymphoblastic Leukemia Using Artificial Intelligence\u2010Oriented Deep Learning Methods"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7423-8853","authenticated-orcid":false,"given":"Sorayya","family":"Rezayi","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7586-9227","authenticated-orcid":false,"given":"Niloofar","family":"Mohammadzadeh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3842-3554","authenticated-orcid":false,"given":"Hamid","family":"Bouraghi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1315-794X","authenticated-orcid":false,"given":"Soheila","family":"Saeedi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8796-8513","authenticated-orcid":false,"given":"Ali","family":"Mohammadpour","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2021,11,11]]},"reference":[{"key":"e_1_2_11_1_2","doi-asserted-by":"publisher","DOI":"10.1111\/j.1601-0825.1997.tb00006.x"},{"key":"e_1_2_11_2_2","doi-asserted-by":"publisher","DOI":"10.1182\/blood-2011-04-347872"},{"key":"e_1_2_11_3_2","doi-asserted-by":"publisher","DOI":"10.1038\/bcj.2017.53"},{"key":"e_1_2_11_4_2","doi-asserted-by":"publisher","DOI":"10.1200\/jco.2016.70.7836"},{"key":"e_1_2_11_5_2","doi-asserted-by":"publisher","DOI":"10.1056\/nejmra052603"},{"key":"e_1_2_11_6_2","doi-asserted-by":"publisher","DOI":"10.3390\/diagnostics10121064"},{"key":"e_1_2_11_7_2","doi-asserted-by":"crossref","unstructured":"MohapatraS.andPatraD. 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The Cancer Imaging Archive."},{"key":"e_1_2_11_17_2","doi-asserted-by":"crossref","unstructured":"HeK. ZhangX. RenS. andSunJ. Deep residual learning for image recognition Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition June 2016 Las Vegas NV USA https:\/\/doi.org\/10.1109\/cvpr.2016.90 2-s2.0-84986274465.","DOI":"10.1109\/CVPR.2016.90"},{"key":"e_1_2_11_18_2","volume-title":"Hierarchical Convolutional Deep Learning in Computer Vision","author":"Zeiler M. D.","year":"2013"},{"key":"e_1_2_11_19_2","doi-asserted-by":"crossref","unstructured":"KassaniS. H. KassaniP. H. WesolowskiM. J. SchneiderK. A. andDetersR. 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