{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,15]],"date-time":"2026-04-15T17:22:59Z","timestamp":1776273779612,"version":"3.50.1"},"reference-count":104,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T00:00:00Z","timestamp":1772755200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003141","name":"SECIHTI","doi-asserted-by":"publisher","award":["1347637"],"award-info":[{"award-number":["1347637"]}],"id":[{"id":"10.13039\/501100003141","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["clinicalkey.com","clinicalkey.com.au","clinicalkey.es","clinicalkey.fr","clinicalkey.jp","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Artificial Intelligence in Medicine"],"published-print":{"date-parts":[[2026,6]]},"DOI":"10.1016\/j.artmed.2026.103393","type":"journal-article","created":{"date-parts":[[2026,3,7]],"date-time":"2026-03-07T01:27:14Z","timestamp":1772846834000},"page":"103393","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":1,"special_numbering":"C","title":["A systematic review of machine and deep learning techniques for acute lymphoblastic leukemia diagnosis"],"prefix":"10.1016","volume":"176","author":[{"given":"W. Hussain","family":"Shah","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"S. Rafia","family":"Fatima","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"R.","family":"Jaimes-Re\u00e1tegui","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"D.E.","family":"Ar\u00e9valo-Simental","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"P.T.","family":"Villalobos-Guti\u00e9rrez","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2471-2507","authenticated-orcid":false,"given":"A.N.","family":"Pisarchik","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"78","reference":[{"issue":"2","key":"10.1016\/j.artmed.2026.103393_b1","doi-asserted-by":"crossref","first-page":"69","DOI":"10.3322\/caac.20107","article-title":"Global cancer statistics","volume":"61","author":"Jemal","year":"2011","journal-title":"A Cancer J Clin"},{"issue":"4","key":"10.1016\/j.artmed.2026.103393_b2","doi-asserted-by":"crossref","first-page":"655","DOI":"10.1016\/j.hoc.2009.04.009","article-title":"Acute lymphoblastic leukemia","volume":"23","author":"Onciu","year":"2009","journal-title":"Hematol Oncol Clin North Am"},{"issue":"10053","key":"10.1016\/j.artmed.2026.103393_b3","doi-asserted-by":"crossref","first-page":"1545","DOI":"10.1016\/S0140-6736(16)31678-6","article-title":"Global, regional, and national incidence, prevalence, and years lived with disability for 310 acute and chronic diseases and injuries, 1990\u20132015: A systematic analysis for the Global Burden of Disease Study 2015","volume":"388","author":"Vos","year":"2016","journal-title":"Lancet"},{"issue":"5","key":"10.1016\/j.artmed.2026.103393_b4","doi-asserted-by":"crossref","DOI":"10.1016\/j.modpat.2024.100466","volume":"37","author":"Choi","year":"2024","journal-title":"Mod Pathol"},{"issue":"12","key":"10.1016\/j.artmed.2026.103393_b5","doi-asserted-by":"crossref","DOI":"10.1016\/j.isci.2024.111356","article-title":"Global, regional, and national burden of acute lymphoblastic leukemia in children: Epidemiological trends analysis from 1990 to 2021","volume":"27","author":"Hu","year":"2024","journal-title":"IScience"},{"key":"10.1016\/j.artmed.2026.103393_b6","doi-asserted-by":"crossref","DOI":"10.3389\/fped.2025.1542649","article-title":"Analysis of global trends in acute lymphoblastic leukemia in children aged 0\u20135 years from 1990 to 2021","volume":"13","author":"Ding","year":"2025","journal-title":"Front Pediatr"},{"issue":"10","key":"10.1016\/j.artmed.2026.103393_b7","doi-asserted-by":"crossref","first-page":"1083","DOI":"10.1634\/theoncologist.2010-0206","article-title":"Obesity and the risk for a hematological malignancy: Leukemia, lymphoma, or myeloma","volume":"15","author":"Lichtman","year":"2010","journal-title":"Oncol"},{"issue":"3","key":"10.1016\/j.artmed.2026.103393_b8","doi-asserted-by":"crossref","first-page":"449","DOI":"10.1016\/j.bbamcr.2015.08.015","article-title":"Advances in understanding the acute lymphoblastic leukemia bone marrow microenvironment: From biology to therapeutic targeting","volume":"1863","author":"Chiarini","year":"2016","journal-title":"Biochim et Biophys Acta (BBA)-Molecular Cell Res"},{"key":"10.1016\/j.artmed.2026.103393_b9","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s12943-016-0518-2","article-title":"Clinical significance of microRNAs in chronic and acute human leukemia","volume":"15","author":"Yeh","year":"2016","journal-title":"Mol Cancer"},{"issue":"5","key":"10.1016\/j.artmed.2026.103393_b10","doi-asserted-by":"crossref","first-page":"991","DOI":"10.1016\/j.hoc.2009.07.001","article-title":"Cytogenetics and molecular genetics of acute lymphoblastic leukemia","volume":"23","author":"Mrozek","year":"2009","journal-title":"Hematol Oncol Clin North Am"},{"issue":"73","key":"10.1016\/j.artmed.2026.103393_b11","first-page":"216","article-title":"The complete blood count in the early diagnosis of acute leukemia in children","volume":"18","author":"Llano","year":"2016","journal-title":"Med Univ"},{"issue":"20","key":"10.1016\/j.artmed.2026.103393_b12","first-page":"2391","article-title":"The 2016 revision to the World Health Organization classification of myeloid neoplasms and acute leukemia","volume":"127","author":"Arber","year":"2016","journal-title":"Blood J Am Soc Hematol"},{"key":"10.1016\/j.artmed.2026.103393_b13","series-title":"Blood and bone marrow pathology e-book","first-page":"289","article-title":"Acute lymphoblastic leukemia\/lymphoma and mixed phenotype acute leukemias","author":"Porwit","year":"2011"},{"issue":"4","key":"10.1016\/j.artmed.2026.103393_b14","doi-asserted-by":"crossref","first-page":"451","DOI":"10.1111\/j.1365-2141.1976.tb03563.x","article-title":"Proposals for the classification of the acute leukaemias French-American-British (FAB) co-operative group","volume":"33","author":"Bennett","year":"1976","journal-title":"Br J Haematol"},{"issue":"1","key":"10.1016\/j.artmed.2026.103393_b15","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1007\/s00428-022-03448-8","article-title":"International consensus classification of acute lymphoblastic leukemia\/lymphoma","volume":"482","author":"Duffield","year":"2023","journal-title":"Virchows Arch"},{"issue":"1","key":"10.1016\/j.artmed.2026.103393_b16","article-title":"Computer-aided diagnosis of acute lymphoblastic leukaemia","volume":"2018","author":"Shafique","year":"2018","journal-title":"Comput Math Methods Med"},{"key":"10.1016\/j.artmed.2026.103393_b17","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1016\/j.cmpb.2018.02.005","article-title":"A review of the automated detection and classification of acute leukaemia: Coherent taxonomy, datasets, validation and performance measurements, motivation, open challenges and recommendations","volume":"158","author":"Alsalem","year":"2018","journal-title":"Comput Methods Programs Biomed"},{"key":"10.1016\/j.artmed.2026.103393_b18","doi-asserted-by":"crossref","DOI":"10.3389\/fonc.2023.1330977","article-title":"Deep learning enhances acute lymphoblastic leukemia diagnosis and classification using bone marrow images","volume":"13","author":"Elsayed","year":"2023","journal-title":"Front Oncol"},{"key":"10.1016\/j.artmed.2026.103393_b19","doi-asserted-by":"crossref","first-page":"16108","DOI":"10.1109\/ACCESS.2023.3245128","article-title":"Deep learning for the detection of acute lymphoblastic leukemia subtypes on microscopic images: A systematic literature review","volume":"11","author":"Mustaqim","year":"2023","journal-title":"IEEE Access"},{"issue":"5","key":"10.1016\/j.artmed.2026.103393_b20","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0096407","article-title":"Blinded by prisma: are systematic reviewers focusing on prisma and ignoring other guidelines?","volume":"9","author":"Fleming","year":"2014","journal-title":"PLoS One"},{"key":"10.1016\/j.artmed.2026.103393_b21","doi-asserted-by":"crossref","first-page":"1005","DOI":"10.3389\/fbioe.2020.01005","article-title":"Automated detection of acute lymphoblastic leukemia from microscopic images based on human visual perception","volume":"8","author":"Bodzas","year":"2020","journal-title":"Front Bioeng Biotechnol"},{"key":"10.1016\/j.artmed.2026.103393_b22","series-title":"2011 18th IEEE international conference on image processing","first-page":"2045","article-title":"All-IDB: The acute lymphoblastic leukemia image database for image processing","author":"Labati","year":"2011"},{"issue":"5","key":"10.1016\/j.artmed.2026.103393_b23","doi-asserted-by":"crossref","first-page":"464","DOI":"10.1055\/s-0043-1764369","article-title":"Role of morphology in the diagnosis of acute leukemias: Systematic review","volume":"44","author":"Sekar","year":"2023","journal-title":"Indian J Med Paediatr Oncol"},{"issue":"4","key":"10.1016\/j.artmed.2026.103393_b24","article-title":"Multi-objective evolutionary algorithms based operation sequence design for image segmentation","volume":"27","author":"Roa","year":"2024","journal-title":"CLEI Electron J"},{"issue":"6","key":"10.1016\/j.artmed.2026.103393_b25","doi-asserted-by":"crossref","first-page":"59","DOI":"10.9790\/1813-0606015963","article-title":"A study for applications of histogram in image enhancement","volume":"6","author":"Kaur","year":"2017","journal-title":"Internat J Engrg Sci"},{"issue":"3","key":"10.1016\/j.artmed.2026.103393_b26","doi-asserted-by":"crossref","first-page":"682","DOI":"10.1109\/41.925596","article-title":"Performance analysis of image compression using wavelets","volume":"48","author":"Grgic","year":"2001","journal-title":"IEEE Trans Ind Electron"},{"key":"10.1016\/j.artmed.2026.103393_b27","unstructured":"Hariprasath S, Dharani T, Santhi M. Detection of acute lymphocytic leukemia using statistical features. In: 4th international conference on current research in engineering science and technology. Trichy; 2019, p. 7\u201313, Available online at: http:\/\/www.internationaljournalssrg.org\/uploads\/specialissuepdf\/ICCREST\/2019\/ECE\/IJECE-ICCREST-P102-JRCE1119.pdf."},{"issue":"3","key":"10.1016\/j.artmed.2026.103393_b28","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","year":"2015","journal-title":"Front Biomed Technol"},{"issue":"1","key":"10.1016\/j.artmed.2026.103393_b29","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1177\/1066896913517939","article-title":"The wonderful colors of the hematoxylin\u2013eosin stain in diagnostic surgical pathology","volume":"22","author":"Chan","year":"2014","journal-title":"Int J Surg Pathol"},{"issue":"1","key":"10.1016\/j.artmed.2026.103393_b30","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1080\/01478885.2019.1708611","article-title":"Optical density-based image analysis method for the evaluation of hematoxylin and eosin staining precision","volume":"43","author":"Chlipala","year":"2020","journal-title":"J Histotechnol"},{"issue":"3","key":"10.1016\/j.artmed.2026.103393_b31","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1109\/79.489268","article-title":"Blind image deconvolution","volume":"13","author":"Kundur","year":"2002","journal-title":"IEEE Signal Process Mag"},{"key":"10.1016\/j.artmed.2026.103393_b32","first-page":"1","article-title":"Acute lymphoblastic leukemia classification using persistent homology","author":"Shah","year":"2024","journal-title":"Eur Phys J Spec Top"},{"issue":"10","key":"10.1016\/j.artmed.2026.103393_b33","doi-asserted-by":"crossref","first-page":"10615","DOI":"10.1109\/TCYB.2021.3062152","article-title":"An efficient blood-cell segmentation for the detection of hematological disorders","volume":"52","author":"Das","year":"2021","journal-title":"IEEE Trans Cybern"},{"key":"10.1016\/j.artmed.2026.103393_b34","series-title":"Proceedings of 2011 6th international forum on strategic technology","first-page":"733","article-title":"A new method for realizing LOG filter in image edge detection","volume":"vol. 2","author":"Jingbo","year":"2011"},{"key":"10.1016\/j.artmed.2026.103393_b35","series-title":"ISBI 2019 C-NMC challenge: Classification in cancer cell imaging: Select proceedings","first-page":"23","article-title":"Classification of normal and leukemic blast cells in B-ALL cancer using a combination of convolutional and recurrent neural networks","author":"Shah","year":"2019"},{"key":"10.1016\/j.artmed.2026.103393_b36","series-title":"ISBI 2019 C-NMC challenge: Classification in cancer cell imaging: Select proceedings","first-page":"33","article-title":"Deep learning for classifying of white blood cancer","author":"Ding","year":"2019"},{"key":"10.1016\/j.artmed.2026.103393_b37","series-title":"ISBI 2019 C-NMC challenge: Classification in cancer cell imaging: Select proceedings","first-page":"83","article-title":"DeepMEN: Multi-model ensemble network for B-lymphoblast cell classification","author":"Xiao","year":"2019"},{"key":"10.1016\/j.artmed.2026.103393_b38","series-title":"ISBI 2019 C-NMC challenge: Classification in cancer cell imaging: Select proceedings","first-page":"95","article-title":"Multi-streams and multi-features for cell classification","author":"Xie","year":"2019"},{"key":"10.1016\/j.artmed.2026.103393_b39","series-title":"2004 12th European signal processing conference","first-page":"753","article-title":"Feature generation for the cell image recognition of myelogenous leukemia","author":"Osowski","year":"2004"},{"key":"10.1016\/j.artmed.2026.103393_b40","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/j.patrec.2019.03.024","article-title":"Acute lymphoblastic leukemia segmentation using local pixel information","volume":"125","author":"Al-jaboriy","year":"2019","journal-title":"Pattern Recognit Lett"},{"key":"10.1016\/j.artmed.2026.103393_b41","doi-asserted-by":"crossref","first-page":"272","DOI":"10.1016\/j.bspc.2016.11.021","article-title":"Gray level co-occurrence matrix and random forest based acute lymphoblastic leukemia detection","volume":"33","author":"Mishra","year":"2017","journal-title":"Biomed Signal Process Control"},{"issue":"1","key":"10.1016\/j.artmed.2026.103393_b42","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1186\/s13244-023-01413-w","article-title":"Topological data analysis in medical imaging: Current state of the art","volume":"14","author":"Singh","year":"2023","journal-title":"Insights Into Imaging"},{"key":"10.1016\/j.artmed.2026.103393_b43","doi-asserted-by":"crossref","unstructured":"Zomorodian A, Carlsson G. Computing persistent homology. In: Proceedings of the 20th annual symposium on computational geometry. 2004, p. 347\u201356.","DOI":"10.1145\/997817.997870"},{"key":"10.1016\/j.artmed.2026.103393_b44","series-title":"Homology theory: An introduction to algebraic topology","author":"Vick","year":"2012"},{"issue":"1","key":"10.1016\/j.artmed.2026.103393_b45","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1140\/epjds\/s13688-017-0109-5","article-title":"A roadmap for the computation of persistent homology","volume":"6","author":"Otter","year":"2017","journal-title":"EPJ Data Sci"},{"key":"10.1016\/j.artmed.2026.103393_b46","doi-asserted-by":"crossref","unstructured":"Cohen-Steiner D, Edelsbrunner H, Harer J. Stability of persistence diagrams. In: Proceedings of the 21st annual symposium on computational geometry. 2005, p. 263\u201371.","DOI":"10.1145\/1064092.1064133"},{"issue":"2","key":"10.1016\/j.artmed.2026.103393_b47","doi-asserted-by":"crossref","first-page":"391","DOI":"10.1016\/j.patcog.2014.06.023","article-title":"An entropy-based persistence barcode","volume":"48","author":"Chintakunta","year":"2015","journal-title":"Pattern Recognit"},{"key":"10.1016\/j.artmed.2026.103393_b48","doi-asserted-by":"crossref","unstructured":"Singhal V, Singh P. Correlation based feature selection for diagnosis of acute lymphoblastic leukemia. In: Proceedings of the third international symposium on women in computing and informatics. 2015, p. 5\u20139.","DOI":"10.1145\/2791405.2791423"},{"key":"10.1016\/j.artmed.2026.103393_b49","series-title":"A diagnostic model for acute lymphoblastic leukemia using metaheuristics and deep learning methods","author":"Rahmani","year":"2024"},{"key":"10.1016\/j.artmed.2026.103393_b50","doi-asserted-by":"crossref","first-page":"142521","DOI":"10.1109\/ACCESS.2020.3012292","article-title":"Automatic detection of white blood cancer from bone marrow microscopic images using convolutional neural networks","volume":"8","author":"Kumar","year":"2020","journal-title":"IEEE Access"},{"issue":"3","key":"10.1016\/j.artmed.2026.103393_b51","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1016\/j.artmed.2014.09.002","article-title":"Leucocyte classification for leukaemia detection using image processing techniques","volume":"62","author":"Putzu","year":"2014","journal-title":"Artif Intell Med"},{"issue":"23","key":"10.1016\/j.artmed.2026.103393_b52","doi-asserted-by":"crossref","first-page":"35277","DOI":"10.1007\/s11042-023-14899-9","article-title":"Segmentation and classification of white blood cancer cells from bone marrow microscopic images using duplet-convolutional neural network design","volume":"82","author":"Devi","year":"2023","journal-title":"Multimedia Tools Appl"},{"key":"10.1016\/j.artmed.2026.103393_b53","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2020.104065","article-title":"Artificial intelligence-based image classification methods for diagnosis of skin cancer: Challenges and opportunities","volume":"127","author":"Goyal","year":"2020","journal-title":"Comput Biol Med"},{"key":"10.1016\/j.artmed.2026.103393_b54","doi-asserted-by":"crossref","first-page":"242","DOI":"10.1016\/j.ejmp.2021.04.016","article-title":"Artificial intelligence and machine learning for medical imaging: A technology review","volume":"83","author":"Barrag\u00e1n-Montero","year":"2021","journal-title":"Phys Medica"},{"issue":"3","key":"10.1016\/j.artmed.2026.103393_b55","doi-asserted-by":"crossref","first-page":"512","DOI":"10.1016\/j.jacr.2017.12.028","article-title":"Machine learning in medical imaging","volume":"15","author":"Giger","year":"2018","journal-title":"J Am Coll Radiol"},{"issue":"1","key":"10.1016\/j.artmed.2026.103393_b56","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1016\/j.imed.2022.07.002","article-title":"Transformers in medical image analysis","volume":"3","author":"He","year":"2023","journal-title":"Intell Med"},{"key":"10.1016\/j.artmed.2026.103393_b57","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1016\/j.media.2017.07.005","article-title":"A survey on deep learning in medical image analysis","volume":"42","author":"Litjens","year":"2017","journal-title":"Med Image Anal"},{"key":"10.1016\/j.artmed.2026.103393_b58","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2022.105894","article-title":"ALNett: A cluster layer deep convolutional neural network for acute lymphoblastic leukemia classification","volume":"148","author":"Jawahar","year":"2022","journal-title":"Comput Biol Med"},{"key":"10.1016\/j.artmed.2026.103393_b59","series-title":"ISBI 2019 C-NMC challenge: Classification in cancer cell imaging: Select proceedings","first-page":"43","article-title":"Ensemble convolutional neural networks for cell classification in microscopic images","author":"Shi","year":"2019"},{"issue":"1","key":"10.1016\/j.artmed.2026.103393_b60","article-title":"Hybrid inception v3 XGBoost model for acute lymphoblastic leukemia classification","volume":"2021","author":"Ramaneswaran","year":"2021","journal-title":"Comput Math Methods Med"},{"key":"10.1016\/j.artmed.2026.103393_b61","series-title":"ISBI 2019 C-NMC challenge: Classification in cancer cell imaging: Select proceedings","first-page":"1","article-title":"Classification of normal versus malignant cells in B-ALL white blood cancer microscopic images","author":"Honnalgere","year":"2019"},{"issue":"1","key":"10.1016\/j.artmed.2026.103393_b62","doi-asserted-by":"crossref","DOI":"10.1155\/2021\/5478157","article-title":"Timely diagnosis of acute lymphoblastic leukemia using artificial intelligence-oriented deep learning methods","volume":"2021","author":"Rezayi","year":"2021","journal-title":"Comput Intell Neurosci"},{"key":"10.1016\/j.artmed.2026.103393_b63","doi-asserted-by":"crossref","first-page":"37203","DOI":"10.1109\/ACCESS.2023.3266511","article-title":"Lightweight efficientnetb3 model based on depthwise separable convolutions for enhancing classification of leukemia white blood cell images","volume":"11","author":"Batool","year":"2023","journal-title":"IEEE Access"},{"key":"10.1016\/j.artmed.2026.103393_b64","series-title":"ISBI 2019 C-NMC challenge: Classification in cancer cell imaging: Select proceedings","first-page":"53","article-title":"Acute lymphoblastic leukemia classification from microscopic images using convolutional neural networks","author":"Prellberg","year":"2019"},{"issue":"1","key":"10.1016\/j.artmed.2026.103393_b65","doi-asserted-by":"crossref","first-page":"12812","DOI":"10.1038\/s41598-025-97297-5","article-title":"All diagnosis: Can efficiency and transparency coexist? An explainble deep learning approach","volume":"15","author":"Muhammad","year":"2025","journal-title":"Sci Rep"},{"issue":"2","key":"10.1016\/j.artmed.2026.103393_b66","doi-asserted-by":"crossref","first-page":"1298","DOI":"10.11591\/eei.v14i2.9073","article-title":"Explainable deep learning for diagnosing acute lymphocytic leukemia using blood smear images","volume":"14","author":"Darmawan","year":"2025","journal-title":"Bull Electr Eng Informatics"},{"key":"10.1016\/j.artmed.2026.103393_b67","series-title":"Leveraging transfer learning from acute lymphoblastic leukemia (all) pretraining to enhance acute myeloid leukemia (aml) prediction","author":"Duraiswamy","year":"2025"},{"issue":"1","key":"10.1016\/j.artmed.2026.103393_b68","doi-asserted-by":"crossref","DOI":"10.1155\/2021\/7529893","article-title":"Method for diagnosis of acute lymphoblastic leukemia based on vit-cnn ensemble model","volume":"2021","author":"Jiang","year":"2021","journal-title":"Comput Intell Neurosci"},{"key":"10.1016\/j.artmed.2026.103393_b69","series-title":"Medical imaging 2022: Computer-aided diagnosis","first-page":"647","article-title":"Image transformers for classifying acute lymphoblastic leukemia","volume":"vol. 12033","author":"Cho","year":"2022"},{"key":"10.1016\/j.artmed.2026.103393_b70","series-title":"ISBI 2019 C-NMC challenge: Classification in cancer cell imaging: Select proceedings","first-page":"73","article-title":"Neighborhood-correction algorithm for classification of normal and malignant cells","author":"Pan","year":"2019"},{"key":"10.1016\/j.artmed.2026.103393_b71","doi-asserted-by":"crossref","DOI":"10.1016\/j.cmpb.2021.105999","article-title":"A deep learning model (ALNet) for the diagnosis of acute leukaemia lineage using peripheral blood cell images","volume":"202","author":"Bold\u00fa","year":"2021","journal-title":"Comput Methods Programs Biomed"},{"issue":"1","key":"10.1016\/j.artmed.2026.103393_b72","doi-asserted-by":"crossref","first-page":"2536","DOI":"10.1038\/s41598-020-59215-9","article-title":"Efficient classification of white blood cell leukemia with improved swarm optimization of deep features","volume":"10","author":"Sahlol","year":"2020","journal-title":"Sci Rep"},{"issue":"15","key":"10.1016\/j.artmed.2026.103393_b73","doi-asserted-by":"crossref","first-page":"5520","DOI":"10.3390\/s22155520","article-title":"BO-ALLCNN: Bayesian-based optimized CNN for acute lymphoblastic leukemia detection in microscopic blood smear images","volume":"22","author":"Atteia","year":"2022","journal-title":"Sensors"},{"issue":"4","key":"10.1016\/j.artmed.2026.103393_b74","doi-asserted-by":"crossref","first-page":"1629","DOI":"10.3390\/s22041629","article-title":"Multi-method diagnosis of blood microscopic sample for early detection of acute lymphoblastic leukemia based on deep learning and hybrid techniques","volume":"22","author":"Abunadi","year":"2022","journal-title":"Sensors"},{"issue":"1","key":"10.1016\/j.artmed.2026.103393_b75","article-title":"IoMT-based automated detection and classification of leukemia using deep learning","volume":"2020","author":"Bibi","year":"2020","journal-title":"J Healthcare Eng"},{"key":"10.1016\/j.artmed.2026.103393_b76","doi-asserted-by":"crossref","DOI":"10.1177\/1533033818802789","article-title":"Acute lymphoblastic leukemia detection and classification of its subtypes using pretrained deep convolutional neural networks","volume":"17","author":"Shafique","year":"2018","journal-title":"Technol Cancer Res Treat"},{"issue":"2","key":"10.1016\/j.artmed.2026.103393_b77","doi-asserted-by":"crossref","first-page":"121","DOI":"10.54392\/irjmt25210","article-title":"An efficient system for detection and classification of acute lymphoblastic leukemia using semi-supervised segmentation technique","volume":"7","author":"Mantri","year":"2025","journal-title":"Int Res J Multidiscip Technovation"},{"key":"10.1016\/j.artmed.2026.103393_b78","series-title":"2020 IEEE-HYDCON","first-page":"1","article-title":"Detection and classification of acute lymphocytic leukemia","author":"Das","year":"2020"},{"key":"10.1016\/j.artmed.2026.103393_b79","series-title":"Enhancing leukemia detection: An automated approach using deep learning and ensemble techniques","author":"Syed","year":"2024"},{"issue":"1","key":"10.1016\/j.artmed.2026.103393_b80","doi-asserted-by":"crossref","first-page":"28056","DOI":"10.1038\/s41598-025-12361-4","article-title":"Acute lymphoblastic leukemia diagnosis using machine learning techniques based on selected features","volume":"15","author":"Houby","year":"2025","journal-title":"Sci Rep"},{"issue":"13","key":"10.1016\/j.artmed.2026.103393_b81","doi-asserted-by":"crossref","first-page":"3376","DOI":"10.3390\/cancers15133376","article-title":"Explainable cad system for classification of acute lymphoblastic leukemia based on a robust white blood cell segmentation","volume":"15","author":"Resendiz","year":"2023","journal-title":"Cancers"},{"key":"10.1016\/j.artmed.2026.103393_b82","article-title":"Rna-biolens: A novel raspberry pi-based digital microscope with image processing for acute lymphoblastic leukemia detection","author":"Sanjaya","year":"2025","journal-title":"IEEE Access"},{"issue":"16","key":"10.1016\/j.artmed.2026.103393_b83","first-page":"6888","article-title":"Na\u00efve Bayesian classifier for acute lymphocytic leukemia detection","volume":"10","author":"Selvaraj","year":"2015","journal-title":"ARPN J Eng Appl Sci"},{"issue":"2","key":"10.1016\/j.artmed.2026.103393_b84","first-page":"81","article-title":"Classification of blood cells from blood cell images using dense convolutional network","volume":"2","author":"Bozkurt","year":"2021","journal-title":"J Sci Technol Eng Res"},{"issue":"1","key":"10.1016\/j.artmed.2026.103393_b85","doi-asserted-by":"crossref","first-page":"2924","DOI":"10.1038\/s41598-022-06718-2","article-title":"Automated human cell classification in sparse datasets using few-shot learning","volume":"12","author":"Walsh","year":"2022","journal-title":"Sci Rep"},{"issue":"1","key":"10.1016\/j.artmed.2026.103393_b86","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1080\/21691401.2021.1879823","article-title":"Classification of white blood cells using weighted optimized deformable convolutional neural networks","volume":"49","author":"Yao","year":"2021","journal-title":"Artif Cells, Nanomedicine, Biotechnol"},{"issue":"1","key":"10.1016\/j.artmed.2026.103393_b87","doi-asserted-by":"crossref","first-page":"37","DOI":"10.24138\/jcomss.v16i1.818","article-title":"Improved white blood cells classification based on pre-trained deep learning models","volume":"16","author":"Mohamed","year":"2020","journal-title":"J Commun Softw Syst"},{"key":"10.1016\/j.artmed.2026.103393_b88","doi-asserted-by":"crossref","first-page":"29252","DOI":"10.1109\/ACCESS.2024.3368031","article-title":"A framework for early detection of acute lymphoblastic leukemia and its subtypes from peripheral blood smear images using deep ensemble learning technique","volume":"12","author":"Perveen","year":"2024","journal-title":"IEEE Access"},{"issue":"1","key":"10.1016\/j.artmed.2026.103393_b89","doi-asserted-by":"crossref","first-page":"17447","DOI":"10.1038\/s41598-024-67826-9","article-title":"An attention-based deep learning for acute lymphoblastic leukemia classification","volume":"14","author":"Jawahar","year":"2024","journal-title":"Sci Rep"},{"issue":"8","key":"10.1016\/j.artmed.2026.103393_b90","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pcbi.1011329","article-title":"The shape of cancer relapse: Topological data analysis predicts recurrence in paediatric acute lymphoblastic leukaemia","volume":"19","author":"Chuli\u00e1n","year":"2023","journal-title":"PLoS Comput Biol"},{"issue":"1","key":"10.1016\/j.artmed.2026.103393_b91","doi-asserted-by":"crossref","first-page":"17","DOI":"10.3390\/cancers13010017","article-title":"High-dimensional analysis of single-cell flow cytometry data predicts relapse in childhood acute lymphoblastic leukaemia","volume":"13","author":"Chuli\u00e1n","year":"2020","journal-title":"Cancers"},{"issue":"1","key":"10.1016\/j.artmed.2026.103393_b92","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1179\/1743131X11Y.0000000057","article-title":"Diagnosis of acute lymphoblastic leukaemia using fuzzy logic and neural networks","volume":"61","author":"Ordaz-Gutierrez","year":"2013","journal-title":"Imaging Sci J"},{"issue":"2","key":"10.1016\/j.artmed.2026.103393_b93","first-page":"94","article-title":"Analysis & classification of acute lymphoblastic leukemia using knn algorithm","volume":"5","author":"Gumble","year":"2017","journal-title":"Int J Recent Innov Trends Comput Commun"},{"issue":"8","key":"10.1016\/j.artmed.2026.103393_b94","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0237428","article-title":"Active semi-supervised learning for biological data classification","volume":"15","author":"Camargo","year":"2020","journal-title":"PLoS One"},{"issue":"5439","key":"10.1016\/j.artmed.2026.103393_b95","doi-asserted-by":"crossref","first-page":"531","DOI":"10.1126\/science.286.5439.531","article-title":"Molecular classification of cancer: Class discovery and class prediction by gene expression monitoring","volume":"286","author":"Golub","year":"1999","journal-title":"Science"},{"issue":"6","key":"10.1016\/j.artmed.2026.103393_b96","doi-asserted-by":"crossref","first-page":"3017","DOI":"10.1364\/BOE.8.003017","article-title":"Spectral-spatial feature-based neural network method for acute lymphoblastic leukemia cell identification via microscopic hyperspectral imaging technology","volume":"8","author":"Wang","year":"2017","journal-title":"Biomed Opt Express"},{"issue":"2","key":"10.1016\/j.artmed.2026.103393_b97","doi-asserted-by":"crossref","first-page":"322","DOI":"10.3390\/electronics12020322","article-title":"A customized efficient deep learning model for the diagnosis of acute leukemia cells based on lymphocyte and monocyte images","volume":"12","author":"Ansari","year":"2023","journal-title":"Electronics"},{"issue":"5","key":"10.1016\/j.artmed.2026.103393_b98","article-title":"Confusion matrix in binary classification problems: A step-by-step tutorial","volume":"6","author":"Amin","year":"2022","journal-title":"J Eng Res"},{"key":"10.1016\/j.artmed.2026.103393_b99","doi-asserted-by":"crossref","DOI":"10.1016\/j.ebiom.2024.105171","article-title":"An artificial intelligence-assisted clinical framework to facilitate diagnostics and translational discovery in hematologic neoplasia","volume":"104","author":"Tang","year":"2024","journal-title":"EBioMedicine"},{"issue":"10","key":"10.1016\/j.artmed.2026.103393_b100","doi-asserted-by":"crossref","first-page":"1264","DOI":"10.1002\/ajh.26295","article-title":"An artificial intelligence-assisted diagnostic platform for rapid near-patient hematology","volume":"96","author":"Bachar","year":"2021","journal-title":"Am J Hematol"},{"issue":"6","key":"10.1016\/j.artmed.2026.103393_b101","doi-asserted-by":"crossref","first-page":"1408","DOI":"10.1111\/ijlh.13681","article-title":"Evaluation of scopio labs x100 full field pbs: The first high-resolution full field viewing of peripheral blood specimens combined with artificial intelligence-based morphological analysis","volume":"43","author":"Katz","year":"2021","journal-title":"Int J Lab Hematol"},{"key":"10.1016\/j.artmed.2026.103393_b102","doi-asserted-by":"crossref","DOI":"10.7717\/peerj-cs.2600","article-title":"ALL-Net: integrating CNN and explainable-AI for enhanced diagnosis and interpretation of acute lymphoblastic leukemia","volume":"11","author":"Thiriveedhi","year":"2025","journal-title":"PeerJ Comput Sci"},{"key":"10.1016\/j.artmed.2026.103393_b103","doi-asserted-by":"crossref","DOI":"10.1109\/ACCESS.2025.3542609","article-title":"LEU3: An attention augmented-based model for acute lymphoblastic leukemia classification","author":"Dutta","year":"2025","journal-title":"IEEE Access"},{"key":"10.1016\/j.artmed.2026.103393_b104","first-page":"1","article-title":"VisTA: Vision transformer-attention enhanced CNN ensemble for optimized classification of acute lymphoblastic leukemia benign and progressive malignant stages","author":"Nunna","year":"2024","journal-title":"Int J Inf Technol"}],"container-title":["Artificial Intelligence in Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S093336572600045X?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S093336572600045X?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,4,14]],"date-time":"2026-04-14T13:38:08Z","timestamp":1776173888000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S093336572600045X"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6]]},"references-count":104,"alternative-id":["S093336572600045X"],"URL":"https:\/\/doi.org\/10.1016\/j.artmed.2026.103393","relation":{},"ISSN":["0933-3657"],"issn-type":[{"value":"0933-3657","type":"print"}],"subject":[],"published":{"date-parts":[[2026,6]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"A systematic review of machine and deep learning techniques for acute lymphoblastic leukemia diagnosis","name":"articletitle","label":"Article Title"},{"value":"Artificial Intelligence in Medicine","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.artmed.2026.103393","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 The Authors. Published by Elsevier B.V.","name":"copyright","label":"Copyright"}],"article-number":"103393"}}