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RESEARCH ARTICLE

Automated classification of pediatric acute lymphoblastic leukemia: A ResNet-50 deep learning approach

Oluwaseun Olumide Okundalaye1†* ,  Necati Ozdemir2† ,  Akintayo Emmanuel Akinsunmade3 ,  Oluwaseun Abiodun Onuoha4 ,  Mario Raso4
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1 Department of Mathematical Sciences, Faculty of Science, Adekunle Ajasin University, Akungba-Akoko, Ondo, Nigeria
2 Department of Mathematics, Faculty of Science and Letters, Balikesir University, Balikesir, Turkiye
3 Department of Mathematical and Computer Sciences, Faculty of Sciences, University of Medical Sciences, Ondo, Nigeria
4 Department of Computer Science, Faculty of Information Engineering,Computer Science and Statistics, Sapienza University of Rome, Rome, Italy
†These authors contributed equally to this work.
IJOCTA, 025340145 https://doi.org/10.36922/IJOCTA025340145
Received: 22 August 2025 | Revised: 31 October 2025 | Accepted: 11 November 2025 | Published online: 9 January 2026
© 2026 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution -Noncommercial 4.0 International License (CC-by the license) ( https://creativecommons.org/licenses/by-nc/4.0/ )
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Abstract

Early detection of acute lymphoblastic leukemia (ALL) is crucial for improving survival outcomes in children. Manual diagnosis through microscopic examination is often time-consuming and subject to human error. This study presents an automated classification framework for pediatric ALL using a fine-tuned Residual Network (ResNet)-50 deep learning architecture. The model was trained and validated on 15,135 segmented blood smear images collected from 118 pediatric patients in the publicly available ALL IDB Version 2 dataset. Data augmentation and patient-wise splitting were applied to ensure model generalization and prevent data leakage. The fine-tuned ResNet-50 achieved a mean classification accuracy of 99.60%, with precision, recall, and F1-score of 99.45%, 99.40%, and 99.42%, respectively, outperforming baseline convolutional neural network models. Statistical validation (p < 0.0015) confirmed that these performance improvements are highly significant. This study highlights the potential of ResNet-50 for reliable, automated, and reproducible leukemia diagnosis, offering clinical decision support for early detection and treatment planning.

Graphical abstract
Keywords
Acute lymphoblastic leukemia
Automated diagnosis
Childhood cancer
Convolutional neural network
Machine learning
Funding
None.
Conflict of interest
Necati Ozdemir is an Editorial Board Member of this journal but was not in any way involved in the editorial and peer-review process conducted for this paper, directly or indirectly. The authors declare they have no competing interests.
References
  1. Bhargavav M. Acute lymphoblastic leukemia. In: Hematologic Malignancies: Case Studies in Cytogenetic and Molecular Genetics. Singapore:  Springer Singapore; 2021;151-193. https://doi.org/10.1007/978-981-33-4799-1

 

  1. Genovese A, Hosseini MS, Piuri V, Plataniotis KN, Scotti F. Acute lymphoblastic leukemia detection based on adaptive un- sharpening and deep learning. In: ICASSP 2021-2021 IEEE  International Conference on Acoustics, Speech and Signal Processing (ICASSP).  New  York:  IEEE; 2021;1205- 1209.https://doi.org/10.1109/ICASSP39728.9414362

 

  1. Malard F, Mohty M. Acute lymphoblastic leukaemia. Lancet. 2020;395:(10230):1146-1162. https://doi.org/10.1016/S0140-6736(19)33018-1

 

  1. Leong SP, Witte MH. Lymphangiogenesis: Lymphatic system and lymph nodes; cancer lymphangiogenesis and metastasis. In: Cancer Metastasis Through the Lymphovascular System. New York: Springer; 2022:209-229. https://doi.org/10.1007/978-3-030-93084-4 21

 

  1. Genovese A, Hosseini MS, Piuri V, Plataniotis KN,  Scotti  Histopathological transfer  learning  for  acute  lymphoblastic leukemia  detection.  In:  2021 IEEE International  Conference  on Computational Intelligence and Virtual Environments for Measurement  Systems  and  Applications (CIVEMSA). New York:  IEEE. 2021:1-6. doi: 10.1109/CIVEMSA52099.2021.9493677

 

  1. Jenkins, E. The biology of leukemia: the cancer of the blood. Microrev Cell Mol Biol. 2022; 9.

 

  1. Okikiolu J,  Dillon  R,  Raj  Acute leukaemia.  Medicine. 2021;49(5):274-281. doi.org/10.1016/j.mpmed.2021.02.004

 

  1. Chiorazzi N, Chen SS, Rai KR. Chronic lymphocytic    Cold Spring Harb  Perspect  Med. 2021;11(2):a035220. https://doi.org/10.1101/cshperspect.a035220

 

  1. Osman AE, Deininger MW. Chronic myeloid leukemia: modern therapies, current challenges and future directions. Blood Rev. 2021;49:100825. https://doi.org/10.1016/j.blre.2021.100825

 

  1. Stacy NI, Harvey JW. Structure of the bone marrow. In: Schalm’s Veterinary Hematology. Hoboken, NJ: Wiley-Blackwell; 2022;18-26. https://doi.org/10.1002/9781119500537.ch3

 

  1. Li R, Ma J, Chan Y, Yang Q, Zhang C. Symptom  clusters  and  influencing  factors in children with acute leukemia during chemotherapy. Cancer Nurs. 2022;43(5):411-418. https://doi.org/10.1097/NCC.0000000000000716

 

  1. Hao TK,  Hiep  PN,  Hoa  NT,  Ha  Causes of death in childhood acute lymphoblastic  leukemia  at  Hue  Central  Hospital  for  10  years  (2008–2018).  Glob Pediatr   Health. 2020;7:2333794X20901930. https://doi.org/10.1177/2333794X20901930

 

  1. Varghese N. Machine learning techniques for the classification of blood cells and the prediction of Int J Comput Sci Eng. 2020;9(1):66-75.

 

  1. Khalil AJ, Abu-Naser SS. Diagnosis of blood cells using deep learning. J. Acad. Eng. Res. 2022;6(2):69-84.

 

  1. V´elez-Rodr´ıguez I,  Carranza-Aranda  Damage mechanism of CK2 and IKAROS in  Philadelphia-like  acute lymphoblastic leukemia.  J Biosci Med. 2024;12(4):49-59. https://doi.org/10.4236/jbm.2024.124005

 

  1. Shiraz P, Jehangir W, Agrawal V. T-cell acute lymphoblastic leukemia—current concepts  in  molecular  biology  and management.   2021;9(11):1621. https://doi.org/10.3390/biomedicines9111621

 

  1. Khan S, Sajjad M, Hussain T, Ullah A, Imran    A  review  on  traditional machine  learning  and  deep  learning models for  WBCs  classification  in  blood  smear images.  IEEE  Access. 2020;9:10657-10673. https://doi.org/10.1109/ACCESS.2020.3048172

 

  1. Yeasmin MN,  Amin  M,  Joti  TJ,  Aung Z,  Azim    Advances  of  AI  in image-based    computer-aided    diagnosis:   a  review.  Array.  2024;3:100357. https://doi.org/10.1016/j.array.2024.100357

 

  1. Kumar A, Tyagi V, Singh H, Jain S. Advancing medical diagnostics on computer-assisted analysis for digital medicinal imagery. In: Computer-Assisted Analysis for Digital Medicinal Imagery. Hershey, PA: IGI Global; 2025:393-408. https://doi.org/10.4018/979-8-3693-5226-7.ch015

 

  1. Zangana HM,  Mohammed  AK, Mustafa FM.  Advancements  and  applications of convolutional  neural  networks  in  im- age  analysis:   a  comprehensive review. J  Ilm  Comput  2024;3(1):16-29. https://doi.org/10.58602/jics.v3i1.30

 

  1. Aydın S,   Altun      Early prediction  of  fabric  quality  using  machine learning  to  reduce  rework  in manufacturing   processes.   Int.   J. Op- timiz.    Contro. 2024;14(4):308-321. https://doi.org/10.11121/ijocta.1462

 

  1. Ghazi FF,  Tawfiq    Design optimal neural  network  based  on  new  LM training  algorithm  for  solving  3D-PDEs. Int J  Optimiz  Contro. 2024;14(3):249-260. https://doi.org/10.11121/ijocta.1519

 

  1. Paswan S, Rathore YK. Detection and classification of blood cancer from microscopic cell images using SVM KNN and NN classifier. Int J Adv Res Ideas Innov Technol. 2017;3:315-324.

 

  1. Calgan   Optimal  C-type  filter design  for  wireless  power  transfer  system by  using  support  vector  machines.  Int J Optimiz Contro. 2023;13(2):151-160. https://doi.org/10.11121/ijocta.2023.1354

 

  1. Abd Halim NH, Mashor MY, Hassan R. Classification of  acute  leukemia  based  on multilayer    J Phys Conf Ser. 2019;1372(1). https://doi.org/10.1088/1742-6596/1372/1/012044

 

  1. Dasariraju S, Huo M, McCalla S. Detection and classification of immature leukocytes for diagnosis of acute myeloid leukemia using random forest algorithm. 2020;7(4):120. https://doi.org/10.3390/bioengineering7040120

 

  1. Prakisya NP, Liantoni F, Hatta P, Aristyagama YH, Setiawan A.  Utilization  of K-nearest neighbor  algorithm  for  classification of white  blood  cells  in  AML  M4, M5, and  M7.  Open  Eng. 2021;11(1):662-668. https://doi.org/10.1515/eng-2021-0065

 

  1. Jafarpisheh N, Zaferani EJ, Teshnehlab M, Karimipour H, Parizi  RM,  Srivastava  G. A deep neural network combined with radial basis function for abnormality classification. Mobile Netw Appl. 2021;26(6):2318- 2328. https://doi.org/10.1007/s11036-021-01835-0

 

  1. Rismayanti N, Naswin A, Zaky U, Zakariyah M, Purnamasari DA. Evaluating thresholding- based segmentation and humoment feature ex- traction in acute lymphoblastic leukemia classification using Gaussian na¨ıve Bayes. Int J Artif Intell Med Issues. 2023;1(2):74-83. https://doi.org/10.56705/ijaimi.v1i2.99

 

  1. Yan T, Wong PK, Ren H, Wang H, Wang J, Li Y. Automatic distinction between COVID- 19 and common pneumonia using multi-scale convolutional neural network on chest CT scans. Chaos Solitons Fractals. 2020;140:110153. https://doi.org/10.1016/j.chaos.2020.110153

 

  1. Kumar A, Kumar M, Goswami P. Numerical solution of coupled system of Emden-Fowler equations using artificial neural network technique. Int J Optimiz Contro. 2024;14(1):62-73.

 

  1. Liu Y, Pu H, Sun, DW. Efficient extraction of deep image features using convolutional neural network (CNN) for applications in detecting and analysing complex food matrices. Trends Food Sci Technol. 2021;113:193-204. https://doi.org/10.1016/j.tifs.2021.04.042

 

  1. Hussain E, Hasan M, Rahman MA, Lee I, Tamanna T, Parvez MZ. CoroDet: a deep learning-based classification  for COVID- 19  detection  using  chest  X-ray images. Chaos  Solitons  Fractals. 2021;142:110495. https://doi.org/10.1016/j.chaos.2020.110495

 

  1. Sipes R, Li  D.  Using convolutional neural  networks  for  automated fine- grained    image 753848415classifica- tion753848415CECE7538484151205634038AU: In:  2018 3rd International Conference on Computational Intelligence and Applications (ICCIA). New York:  IEEE; 2018:157-161. https://doi.org/10.1109/ICCIA.2018.00036

 

  1. Shafique S, Tehsin S. Acute lymphoblastic leukemia detection  and classification of its subtypes using pretrained deep convolutional  neural  networks.  Technol Cancer  Res  Treat.  2018;17:1533033818802789. https://doi.org/10.1177/1533033818802789

 

  1. Pansombut T, Wikaisuksakul S, Khongkraphan K, Phon-On A. Convolutional neural networks for recognition of lymphoblast cell images. Comput Intell Neurosci. 2019;(1):7519603. https://doi.org/10.1155/2019/7519603

 

  1. de Oliveira JEM, Dantas DO. Classification of normal versus leukemic cells with data augmentation and convolutional neural networks. In: Proceeding of the VISIGRAPP. Setu´bal, Portugal:  SciTePress; 4:685-692. https://doi.org/10.5220/0010257406850692

 

  1. Zolfaghari M, Sajedi H. A survey on automated detection and classification of acute leukemia and WBCs in microscopic blood cells. Multimed Tools Appl. 2020;81:6723-6753. https://doi.org/10.1007/s11042-022-12108-7

 

  1. Huang Y, Zhang J, Liu R, Zhao S. Improving accuracy and  interpretability  of CNN-based fault diagnosis through an attention mechanism. Processes. 2023;11(11):3233. https://doi.org/10.3390/pr11113233

 

  1. Kadhim KA, Najjar FH, Waad AA, Al-Kharsan IH, Khudhair ZN, Salim AA. Leukemia classification using a convolutional neural network of AML images. Mal J Fund Appl Sci. 2023;19(3):306-312. https://doi.org/10.11113/mjfas.v19n3.2901

 

  1. Kanavati F, Ichihara S, Tsuneki M. A deep learning model for  breast  ductal  carcinoma in situ classification in whole slide images. Virchows Arch., 2022;480(5):1009-1022. https://doi.org/10.1007/s00428-021-03241-z

 

  1. Mustaqim T, Fatichah C, Suciati N. Deep learning for the  detection  of  acute  lymphoblastic  leukemia  subtypes  on microscopic images:  a systematic literature re- view.  IEEE  Access. 2023;11:16108-16127. https://doi.org/10.1109/ACCESS.2023.3245128

 

  1. Genovese A, Piuri V, Scotti F. A decision sup- port system for acute lymphoblastic leukemia detection based on explainable artificial intelligence. Image Vis Comput. 2024;151:105298. https://doi.org/10.1016/j.imavis.2024.105298

 

  1. Archana R, Jeevaraj  PE.  Deep learning  models  for  digital  image processing: A  review.  Artif  Intell  Rev.  2024;57:11. https://doi.org/10.1007/s10462-023-10631-z

 

  1. Talukder MA, Layek MA, Kazi M, Uddin MA, Aryal S. Empowering COVID-19 detection: optimizing performance  through fine-tuned  efficient net  deep  learning  architecture. Comput Biol Med. 2024;168:107789. https://doi.org/10.1016/j.compbiomed.2023.107789

 

  1. Genovese A. ALLNet: acute lymphoblastic leukemia  detection  using  lightweight convolutional  networks.  In:   2022 IEEE 9th International Conference on Computational Intelligence and Virtual Environments for  Measurement  Systems  and Applica- tions (CIVEMSA),  New York; 2022:1-6. https://doi.org/10.1109/CIVEMSA53371.2022.9853691

 

  1. Kochhar A, Kaur P. Leveraging transfer learning for acute lymphoblastic leukemia cell classification: a deep learning approach. In: International Conference on Innovative Computing and Communication. Singapore: Springer Nature Singa- pore; 2025:187-200. https://doi.org/10.1007/978- 981-96-7137-3 14

 

  1. Muduli D, Parija S, Kumari S, Hassan A, Jangwan HS, Zamani  AT,  et  al. Deep learning-based  detection  and classification of  acute  lymphoblastic  leukemia  with  explainable  AI  techniques.  Array. 2025;26. https://doi.org/10.1016/j.array.2025.100397

 

  1. El Houby EM. Acute lymphoblastic leukemia diagnosis using machine learning techniques based on selected features. Sci Rep. 2025;15(1):28056. https://doi.org/10.1038/s41598-025-12361-4

 

  1. Harithanush S, Poojitha K, Shalini R, Vandana B, Nithya  R. LeukemiaVisionNet-21 deep neural network for automated acute lymphoblastic  leukemia  (ALL) classification. In: 2025 3rd International Conference on Data Science and Information Systems (ICDSIS).  New  York:   IEEE; 2025:1-7. https://doi.org/10.1109/ICDSIS65355.2025.11070368

 

  1. Genovese A, Piuri  V,  Scotti  F.  ALL- IDB  patches:   whole  slide  imaging for acute  lymphoblastic  leukemia detection using deep learning. In:  2023 IEEE International  Conference  on  Acoustics, Speech,  and  Signal  Processing  Workshops (ICASSPW),  New  York:  IEEE; 2023:1-5. https://doi.org/10.1109/ICASSPW59220.2023.10193429

 

  1. Okundalaye OO, O¨ zdemir N, Evirgen F. Leveraging machine learning for early and accurate anaemia diagnosis: a comparative study of classification algorithms. In: International Conference on Mathematical Modelling, Applied Analysis and Computation. Cham: Springer Nature; 2025:42- 52.

 

  1. Okundalaye O, Ozdemir   N,   Rotimi B,   Akanbi   F.   Climate-based predictive   modeling   of   malaria incidence   using   statistical   and machine learning    approaches.    Trans Comput  Model  Intell  Syst. 2025;1:10014. https://doi.org/10.65112/tcmis.10014

 

  1. Sher M, Shah  K,  Ali  Z,  Abdeljawad T,   Alqudah   M.   Using   deep neural  network  in  computational analysis  of  coupled  systems  of fractional integro-differential   equations.   J Com- put   Appl   Math. 2025;474:116912. https://doi.org/10.1016/j.cam.2025.116912

 

  1. Emin B, Alaca Y, Akme¸se O¨ F, Karaca Y, Akgu¨l A. Pixel map-based hybrid ai framework for early and accurate cardiovascular disease diagnosis. J Comput Appl Math. 2026;476:117095. https://doi.org/10.1016/j.cam.2025.117095

 

  1. Hamou AA, Rasul RR, Hammouch Z, O¨ zdemir N. Analysis and dynamics of a mathematical model to predict unreported cases of COVID-19 epidemic in Morocco. Comp Appl Math. 2022;41:289. https://doi.org/10.1007/s40314-022-01990-4

 

  1. Genovese A, Piuri V, Plataniotis KN, Scotti F. DL4ALL: multi-task cross-dataset transfer learning for acute lymphoblastic leukemia detection. IEEE Access. 2023;11:65222-65237. https://doi.org/10.1109/ACCESS.2023.3289219

 

  1. Okundalaye OO, O¨ zdemir N, Awonusika RO. Early breast cancer prediction using optimized machine learning and tumor-immune modeling. J Comput Appl Math. 2026;473:116875. https://doi.org/10.1016/j.cam.2025.116875

 

  1. Olumide OO. Fractional-order gradient de- scent for enhancing deep learning optimization. Asian Res J Math. 2025;21(8):40-50. https://doi.org/10.9734/arjom/2025/v21i8970

 

  1. Putzu L, Caocci G, Di Ruberto C. Leucocyte classification for  leukaemia detection  using  image  processing techniques. Artif   Intell   Med. 2014;62(3):179-191. https://doi.org/10.1016/j.artmed.2014.09.002

 

  1. Joshi MD, Karode AH, Suralkar S. White blood cells segmentation and classification to detect acute leukemia. Int J Emerg Trends Technol Comput Sci. 2013;2(3):147-151.

 

  1. Chatap N, Shibu  S.  Analysis  of  blood samples for counting leukemia cells using support vector machine and nearest neigh- bour. IOSR J Comput Eng. 2014;16(5):79-87. https://doi.org/10.9790/0661-16537987

 

  1. MoradiAmin M, Samadzadehaghdam N, Kermani S, Talebi A. Enhanced recognition of acute lymphoblastic leukemia cells in microscopic im- ages based on feature reduction using principle component analysis. Front Biomed Technol. 2015;2(3):128-136.

 

  1. Rawat J, Singh A, Bhadauria HS, Virmani J, Devgun JS. Classification of acute lymphoblastic leukaemia using hybrid hierarchical classifiers. Multimed Tools Appl. 2017;76(18):19057-19085. https://doi.org/10.1007/s11042-017-4478-3
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