{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,2]],"date-time":"2026-08-02T21:37:55Z","timestamp":1785706675556,"version":"3.56.0"},"reference-count":39,"publisher":"Wiley","issue":"7","license":[{"start":{"date-parts":[[2024,3,6]],"date-time":"2024-03-06T00:00:00Z","timestamp":1709683200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"funder":[{"DOI":"10.13039\/501100013778","name":"Aja University of Medical Sciences","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100013778","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["analyticalsciencejournals.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Microscopy Res &amp;amp; Technique"],"published-print":{"date-parts":[[2024,7]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:label\/><jats:p>Acute lymphoblastic leukemia (ALL) is a life\u2010threatening disease that commonly affects children and is classified into three subtypes: L1, L2, and L3. Traditionally, ALL is diagnosed through morphological analysis, involving the examination of blood and bone marrow smears by pathologists. However, this manual process is time\u2010consuming, laborious, and prone to errors. Moreover, the significant morphological similarity between ALL and various lymphocyte subtypes, such as normal, atypic, and reactive lymphocytes, further complicates the feature extraction and detection process. The aim of this study is to develop an accurate and efficient automatic system to distinguish ALL cells from these similar lymphocyte subtypes without the need for direct feature extraction. First, the contrast of microscopic images is enhanced using histogram equalization, which improves the visibility of important features. Next, a fuzzy C\u2010means clustering algorithm is employed to segment cell nuclei, as they play a crucial role in ALL diagnosis. Finally, a novel convolutional neural network (CNN) with three convolutional layers is utilized to classify the segmented nuclei into six distinct classes. The CNN is trained on a labeled dataset, allowing it to learn the distinguishing features of each class. To evaluate the performance of the proposed model, quantitative metrics are employed, and a comparison is made with three well\u2010known deep networks: VGG\u201016, DenseNet, and Xception. The results demonstrate that the proposed model outperforms these networks, achieving an approximate accuracy of 97%. Moreover, the model's performance surpasses that of other studies focused on 6\u2010class classification in the context of ALL diagnosis.<\/jats:p><\/jats:sec><jats:sec><jats:title>Research Highlights<\/jats:title><jats:p><jats:list list-type=\"bullet\">\n<jats:list-item><jats:p>Deep neural networks eliminate the requirement for feature extraction in ALL classification<\/jats:p><\/jats:list-item>\n<jats:list-item><jats:p>The proposed convolutional neural network achieves an impressive accuracy of approximately 97% in classifying six ALL and lymphocyte subtypes.<\/jats:p><\/jats:list-item>\n<\/jats:list><\/jats:p><\/jats:sec>","DOI":"10.1002\/jemt.24551","type":"journal-article","created":{"date-parts":[[2024,3,6]],"date-time":"2024-03-06T04:38:45Z","timestamp":1709699925000},"page":"1615-1626","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Automatic classification of acute lymphoblastic leukemia cells and lymphocyte subtypes based on a novel convolutional neural network"],"prefix":"10.1002","volume":"87","author":[{"given":"Morteza","family":"MoradiAmin","sequence":"first","affiliation":[{"name":"Department of Physiology, Faculty of Medicine AJA University of Medical Sciences  Tehran Iran"},{"name":"Department of Medical Physics and Biomedical Engineering School of Medicine, Tehran University of Medical Sciences (TUMS)  Tehran Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mitra","family":"Yousefpour","sequence":"additional","affiliation":[{"name":"Department of Physiology, Faculty of Medicine AJA University of Medical Sciences  Tehran Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5027-3416","authenticated-orcid":false,"given":"Nasser","family":"Samadzadehaghdam","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering, Faculty of Advanced Medical Sciences Tabriz University of Medical Sciences  Tabriz Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Laya","family":"Ghahari","sequence":"additional","affiliation":[{"name":"Department of Anatomy, Faculty of Medicine AJA University of Medical Sciences  Tehran Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mahdi","family":"Ghorbani","sequence":"additional","affiliation":[{"name":"Department of Medical Laboratory Sciences School of Allied Medical Sciences, AJA University of Medical Sciences  Tehran Iran"},{"name":"Medical Biotechnology Research Center AJA University of Medical Sciences  Tehran Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Majid","family":"Mafi","sequence":"additional","affiliation":[{"name":"Mechanical Engineering Department Iran University of Science and Technology  Tehran Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2024,3,6]]},"reference":[{"key":"e_1_2_9_2_1","doi-asserted-by":"publisher","DOI":"10.1049\/iet-ipr.2018.5910"},{"key":"e_1_2_9_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.heliyon.2018.e00938"},{"key":"e_1_2_9_4_1","doi-asserted-by":"publisher","DOI":"10.4103\/2228-7477.150428"},{"key":"e_1_2_9_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.irbm.2021.05.005"},{"key":"e_1_2_9_6_1","doi-asserted-by":"publisher","DOI":"10.1007\/b106267"},{"key":"e_1_2_9_7_1","doi-asserted-by":"crossref","unstructured":"Bhattacharjee R. &Saini L. M.(2015).Detection of Acute Lymphoblastic Leukemia using watershed transformation technique. Paper presented at the 2015 International conference on signal processing computing and control (ISPCC).","DOI":"10.1109\/ISPCC.2015.7375060"},{"key":"e_1_2_9_8_1","doi-asserted-by":"publisher","DOI":"10.1504\/IJMEI.2019.096892"},{"issue":"2","key":"e_1_2_9_9_1","first-page":"39","article-title":"Extracted haralick's texture features and morphological parameters from segmented multispectrale texture bio\u2010images for classification of colon cancer cells","volume":"8","author":"Chaddad A.","year":"2011","journal-title":"WSEAS Transactions on Biology and Biomedicine"},{"key":"e_1_2_9_10_1","doi-asserted-by":"crossref","unstructured":"Chollet F.(2017).Xception: Deep learning with depthwise separable convolutions. Paper presented at the Proceedings of the IEEE conference on computer vision and pattern recognition.","DOI":"10.1109\/CVPR.2017.195"},{"key":"e_1_2_9_11_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.clml.2021.06.025"},{"key":"e_1_2_9_12_1","doi-asserted-by":"crossref","unstructured":"Ferguson M. Ak R. Lee Y.\u2010T. T. &Law K. H.(2017).Automatic localization of casting defects with convolutional neural networks. Paper presented at the 2017 IEEE international conference on big data (big data).","DOI":"10.1109\/BigData.2017.8258115"},{"key":"e_1_2_9_13_1","doi-asserted-by":"crossref","unstructured":"He K. Zhang X. Ren S. &Sun J.(2016).Deep residual learning for image recognition. Paper presented at the Proceedings of the IEEE conference on computer vision and pattern recognition.","DOI":"10.1109\/CVPR.2016.90"},{"key":"e_1_2_9_14_1","doi-asserted-by":"crossref","unstructured":"Huang G. Liu Z. van derMaaten L. &Weinberger K. Q.(2017).Densely connected convolutional networks. Paper presented at the Proceedings of the IEEE conference on computer vision and pattern recognition.","DOI":"10.1109\/CVPR.2017.243"},{"key":"e_1_2_9_15_1","doi-asserted-by":"publisher","DOI":"10.1002\/jemt.22733"},{"issue":"8","key":"e_1_2_9_16_1","first-page":"3451","article-title":"An evolution based hybrid approach for heart diseases classification and associated risk factors identification","volume":"28","author":"Iftikhar S.","year":"2017","journal-title":"Biomedical Research"},{"key":"e_1_2_9_17_1","doi-asserted-by":"publisher","DOI":"10.1002\/jemt.22867"},{"key":"e_1_2_9_18_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2019.104987"},{"key":"e_1_2_9_19_1","doi-asserted-by":"crossref","unstructured":"Kumar S. Mishra S. Asthana P. &Pragya. (2018).Automated detection of acute leukemia using k\u2010mean clustering algorithm. Paper presented at the Advances in Computer and Computational Sciences: Proceedings of ICCCCS 2016 Volume 2. (pp.655\u2013670).Springer Singapore.","DOI":"10.1007\/978-981-10-3773-3_64"},{"key":"e_1_2_9_20_1","doi-asserted-by":"crossref","unstructured":"Mandal S. Daivajna V. Kalsangra S. Rajagopalan V. &Kuchlous A.(2019).Computer aided system for automatic detection and marking instance of nuclei. Paper presented at the 2019 IEEE international conference on electrical Computer and Communication Technologies (ICECCT).","DOI":"10.1109\/ICECCT.2019.8869414"},{"key":"e_1_2_9_21_1","unstructured":"Mao\u2010jun S. Zhao\u2010bin W. Hong\u2010juan Z. &Yi\u2010de M.(2008).A new method for blood cell image segmentation and counting based on PCNN and autowave. Paper presented at the 2008 3rd International symposium on communications control and signal processing."},{"key":"e_1_2_9_22_1","unstructured":"Mokhtar N. Harun N. H. Mashor M. Y. Mustafa N. Adollah R. &Mohd Nasir N. F.(2009).Image enhancement techniques using local global bright dark and partial contrast stretching for acute leukemia images."},{"key":"e_1_2_9_23_1","doi-asserted-by":"publisher","DOI":"10.1002\/jemt.22718"},{"issue":"3","key":"e_1_2_9_24_1","first-page":"128","article-title":"Enhanced recognition of acute lymphoblastic leukemia cells in microscopic images based on feature reduction using principle component analysis","volume":"2","author":"MoradiAmin M.","year":"2015","journal-title":"Frontiers in Biomedical Technologies"},{"key":"e_1_2_9_25_1","doi-asserted-by":"publisher","DOI":"10.1002\/jemt.22961"},{"key":"e_1_2_9_26_1","unstructured":"Nasir A. A. Mashor M. &Rosline H.(2011).Unsupervised colour segmentation of white blood cell for acute leukaemia images. Paper presented at the 2011 IEEE international conference on imaging systems and techniques."},{"key":"e_1_2_9_27_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.artmed.2014.09.002"},{"key":"e_1_2_9_28_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.procs.2015.10.113"},{"key":"e_1_2_9_29_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11042-017-4478-3"},{"key":"e_1_2_9_30_1","doi-asserted-by":"publisher","DOI":"10.1002\/jemt.23030"},{"key":"e_1_2_9_31_1","doi-asserted-by":"publisher","DOI":"10.1002\/jemt.23139"},{"key":"e_1_2_9_32_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-020-59215-9"},{"key":"e_1_2_9_33_1","doi-asserted-by":"publisher","DOI":"10.1111\/ijlh.13089"},{"key":"e_1_2_9_34_1","doi-asserted-by":"crossref","unstructured":"Saritha M. Prakash B. Sukesh K. &Shrinivas B.(2016).Detection of blood cancer in microscopic images of human blood samples: A review. Paper presented at the 2016 International Conference on Electrical Electronics and Optimization Techniques (ICEEOT).","DOI":"10.1109\/ICEEOT.2016.7754751"},{"key":"e_1_2_9_35_1","doi-asserted-by":"crossref","unstructured":"Scotti F.(2005).Automatic morphological analysis for acute leukemia identification in peripheral blood microscope images. Paper presented at the CIMSA. 2005 IEEE international conference on computational intelligence for measurement systems and applications.","DOI":"10.1109\/CIMSA.2005.1522835"},{"key":"e_1_2_9_36_1","doi-asserted-by":"publisher","DOI":"10.1177\/1533033818802789"},{"key":"e_1_2_9_37_1","unstructured":"Simonyan K. &Zisserman A.(2014).Very deep convolutional networks for large\u2010scale image recognition.arXiv Preprint arXiv:1409.1556."},{"key":"e_1_2_9_38_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2020.106810"},{"key":"e_1_2_9_39_1","doi-asserted-by":"publisher","DOI":"10.5755\/j01.eie.25.5.24358"},{"key":"e_1_2_9_40_1","doi-asserted-by":"publisher","DOI":"10.1002\/jemt.22646"}],"container-title":["Microscopy Research and Technique"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/analyticalsciencejournals.onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/jemt.24551","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,16]],"date-time":"2025-10-16T17:44:55Z","timestamp":1760636695000},"score":1,"resource":{"primary":{"URL":"https:\/\/analyticalsciencejournals.onlinelibrary.wiley.com\/doi\/10.1002\/jemt.24551"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,3,6]]},"references-count":39,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2024,7]]}},"alternative-id":["10.1002\/jemt.24551"],"URL":"https:\/\/doi.org\/10.1002\/jemt.24551","archive":["Portico"],"relation":{},"ISSN":["1059-910X","1097-0029"],"issn-type":[{"value":"1059-910X","type":"print"},{"value":"1097-0029","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,3,6]]},"assertion":[{"value":"2023-09-20","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-02-26","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-03-06","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}