Abstract
Traditionally, researchers, doctors, and hematologists have faced difficulties in making a timely diagnosis of leukemia. Since, unlike other malignancies, leukemia typically does not create a (tumor) that can be seen in imaging techniques like X-rays or CT scans. As a result the procedures available at medical diagnosis centres of leukemia consumes a lot of time. Influenced by the capabilities of artificial intelligence techniques diagnosing the disease, the paper introduces advanced CNN models which includes InceptionV3, DenseNet201, Xception, ResNet152V2, and two hybrid models, i.e., InceptionResNetV2 and XceptionInceptionResNetV2 for identifying and classifying normal and leukemia cancer cells. Accuracy, root mean square error, recall, precision, F1 score, and loss are used to evaluate all applied advanced learning models. The Acute lymphoblastic leukemia dataset, which is separated into two classes: normal cells and malignant leukemia cells, is used in this study. During the pre-processing stage, every image undergo enhancement and are visually shown to fetch the color channels in the shape of an RGB histogram. Later the images are augmented to produce regions of interest by generating extreme points and employing adaptive thresholding techniques before being provided to the applied models for training. During the experimentation, it was discovered that InceptionResNetV2 had the highest validation accuracy of 98.59%. Similarly, DenseNet201 had the highest precision (97.57%), followed by InceptionV3 with recall (95.77%) and F1 score (95.56%). Moreover, the confusion matrix has also been generated to obtain the models’ recall, precision, and F1 score values for different classes of the dataset.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Data availability
Not applicable.
Code availability
Not applicable.
References
Trendowski M (2015) The inherent metastasis of leukaemia and its exploitation by sonodynamic therapy. Crit Rev Oncol/Hematol 94(2):149–163. https://doi.org/10.1016/j.critrevonc.2014.12.013
Blackburn LM, Bender S, Brown S (2019) Acute Leukemia: diagnosis and treatment. Semin Oncol Nurs 35(6):150950. https://doi.org/10.1016/j.soncn.2019.150950
Leukemia - OrthoInfo - AAOS. (n.d.). https://orthoinfo.aaos.org/en/diseases--conditions/leukemia/. Accessed 15 Sep 2022
Bencomo-Alvarez AE, Rubio AJ, Gonzalez MA, Eiring AM (2021) Blood cancer health disparities in the United States hispanic population. Mol Case Stud 7(2):a005967. https://doi.org/10.1101/mcs.a005967
Terwilliger T, Abdul-Hay M (2017) Acute lymphoblastic leukemia: a comprehensive review and 2017 update. Blood Cancer J 7(6):e577. https://doi.org/10.1038/bcj.2017.53
Tebbi CK (2021) Etiology of Acute Leukemia: a review. Cancers 13(9):2256. https://doi.org/10.3390/cancers13092256
Shafique S, Tehsin S (2018) Computer-Aided Diagnosis of Acute Lymphoblastic Leukaemia. Computational and Mathematical Methods in Medicine, 2018, 1–13. https://doi.org/10.1155/2018/6125289
Gupta A, Koul A, Kumar Y (2022) Pancreatic cancer detection using machine and deep learning techniques. In: 2022 2nd International Conference on Innovative Practices in Technology and Management (ICIPTM) (vol. 2). IEEE, pp 151–155. https://ieeexplore.ieee.org/abstract/document/9754010
Mohamed H, Omar R, Saeed N, Essam A, Ayman N, Mohiy T, AbdelRaouf A (2018) Automated detection of white blood cells cancer diseases. In: 2018 First international workshop on deep and representation learning (IWDRL). IEEE, pp 48–54. https://ieeexplore.ieee.org/abstract/document/8358214
Mishra S, Majhi B, Sa PK (2019) Texture feature based classification on microscopic blood smear for acute lymphoblastic leukemia detection. Biomed Signal Process Control 47:303–311. https://doi.org/10.1016/j.bspc.2018.08.012
Aftab MO, Awan MJ, Khalid S, Javed R, Shabir H (2021) Executing spark BigDL for leukemia detection from microscopic images using transfer learning. In 2021 1st International Conference on Artificial Intelligence and Data Analytics (CAIDA). IEEE, pp 216–220. https://ieeexplore.ieee.org/abstract/document/9425264
Saleem S, Amin J, Sharif M, Anjum MA, Iqbal M, Wang SH (2021b) A deep network designed for segmentation and classification of leukemia using fusion of the transfer learning models. Complex Intell Syst 8(4):3105–3120. https://doi.org/10.1007/s40747-021-00473-z
Anilkumar K, Manoj V, Sagi T (2021) Automated detection of leukemia by pretrained deep neural networks and transfer learning: a comparison. Med Eng Phys 98:8–19. https://doi.org/10.1016/j.medengphy.2021.10.006
Mandal S, Daivajna V, Rajagopalan V (2019) Machine learning based system for automatic detection of leukemia cancer cell. In: 2019 IEEE 16th India Council International Conference (INDICON). IEEE, pp 1–4. https://ieeexplore.ieee.org/abstract/document/9029034
Rehman A, Abbas N, Saba T, Rahman SIU, Mehmood Z, Kolivand H (2018) Classification of acute lymphoblastic leukemia using deep learning. Microsc Res Tech 81(11):1310–1317. https://doi.org/10.1002/jemt.23139
Sudha K, Geetha P (2020) A novel approach for segmentation and counting of overlapped leukocytes in microscopic blood images. J Appl Biomed 40(2):639–648. https://doi.org/10.1016/j.bbe.2020.02.005
Acharya V, Kumar P (2019) Detection of acute lymphoblastic leukemia using image segmentation and data mining algorithms. Med Biol Eng Comput 57(8):1783–1811. https://doi.org/10.1007/s11517-019-01984-1
Das PK, Pradhan A, Meher S (2021) Detection of acute lymphoblastic leukemia using machine learning techniques. In: Lecture notes in electrical engineering, pp 425–437. https://doi.org/10.1007/978-981-16-0289-4_32
Das PK, Meher S (2021) Transfer learning-based automatic detection of acute lymphocytic leukemia. In: 2021 National Conference on Communications (NCC). IEEE, pp 1–6. https://ieeexplore.ieee.org/abstract/document/9530010
Loey M, Naman M, Zayed H (2020) Deep transfer learning in diagnosing leukemia in blood cells. Computers 9(2):29. https://doi.org/10.3390/computers9020029
Vogado LH, Veras RM, Araujo FH, Silva RR, Aires KR (2018) Leukemia diagnosis in blood slides using transfer learning in CNNs and SVM for classification. Eng Appl Artif Intell 72:415–422. https://doi.org/10.1016/j.engappai.2018.04.024
Rupapara V, Rustam F, Aljedaani W, Shahzad HF, Lee E, Ashraf I (2022) Blood cancer prediction using leukemia microarray gene data and hybrid logistic vector trees model. Sci Rep 12(1). https://doi.org/10.1038/s41598-022-04835-6
Atteia G, Alhussan A, Samee N (2022) BO-ALLCNN: Bayesian-based optimized CNN for acute lymphoblastic leukemia detection in microscopic blood smear images. Sensors 22(15):5520. https://doi.org/10.3390/s22155520
Mondal C, Hasan MK, Jawad MT, Dutta A, Islam MR, Awal MA, Ahmad M (2021) Acute lymphoblastic leukemia detection from microscopic images using weighted ensemble of convolutional neural networks. arXiv.org. https://arxiv.org/abs/2105.03995
Bendiabdallah MH, Settouti N (2021) A comparison of U-net backbone architectures for the automatic white blood cells segmentation. https://worldascience.org/journals/index.php/wassn/article/view/24
Jamil MMA, Oussama L, Hafizah WM, Wahab MHA, Johan MF (2019) Computational automated system for red blood cell detection and segmentation. Elsevier eBooks, pp 173–189. https://doi.org/10.1016/b978-0-12-815553-0.00008-2
Genovese A, Hosseini MS, Piuri V, Plataniotis KN, Scotti F (2021) 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). IEEE. pp 1–6. https://doi.org/10.1109/CIVEMSA52099.2021.9493677
Boldú L, Merino A, Alférez S, Molina A, Acevedo A, Rodellar J (2019) Automatic recognition of different types of acute leukaemia in peripheral blood by image analysis. J Clin Pathol 72(11):755–761. https://doi.org/10.1136/jclinpath-2019-205949
Duggal R, Gupta A, Gupta R, Wadhwa M, Ahuja C (2016) Overlapping cell nuclei segmentation in microscopic images using deep belief networks. In: Proceedings of the tenth Indian conference on computer vision, graphics and image processing, pp 1–8. https://doi.org/10.1145/3009977.3010043
Gupta S, Kumar Y (2021) Cancer prognosis using artificial intelligence-based techniques. SN Comput Sci 3(1). https://doi.org/10.1007/s42979-021-00964-3
Bhardwaj P, Kumar Y, Bhandari G (2021) AI-Enabled Computational Techniques for Cancer Diagnosis. In: 2021 IEEE 8th Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON). IEEE, pp 1–7. https://ieeexplore.ieee.org/abstract/document/9667624
Ramaneswaran S, Srinivasan K, Vincent PMDR, Chang CY (2021) Hybrid Inception v3 XGBoost model for acute lymphoblastic leukemia classification. Comput Math Methods Med :1–10. https://doi.org/10.1155/2021/2577375
Parayil S, Aravinth J (2022) Transfer learning-based feature fusion of white blood cell image classification. In: 2022 7th International Conference on Communication and Electronics Systems (ICCES). IEEE, pp 1468–1474. https://ieeexplore.ieee.org/abstract/document/9835815
Gupta S, Gupta A, Kumar Y (2021) Artificial intelligence techniques in cancer research: opportunities and challenges. In: 2021 International Conference on Technological Advancements and Innovations (ICTAI). IEEE, pp 411–416. https://ieeexplore.ieee.org/abstract/document/9673174
Cheng FM, Lo SC, Lin CC, Lo WJ, Chien SY, Sun TH, Hsu KC (2024) Deep learning assists in acute leukemia detection and cell classification via flow cytometry using the acute leukemia orientation tube. Sci Rep 14(1):8350. https://doi.org/10.1038/s41598-024-58580-z
Jawahar M, Anbarasi LJ, Narayanan S, Gandomi AH (2024) An attention-based deep learning for acute lymphoblastic leukemia classification. Sci Rep 14(1):17447. https://doi.org/10.1038/s41598-024-67826-9
Kaur N, Singh A (2024) VGG16-PCA-PB3C: a hybrid PB3C and deep neural network based approach for leukemia detection. Int J Inform Technol 1–11. https://doi.org/10.1007/s41870-024-01990-z
Tusar MTHK, Islam MT, Sakil AH, Khandaker MHN, Hossain MM (2024) An Intelligent telediagnosis of acute lymphoblastic leukemia using histopathological deep learning. J Comput Theor Appl 2(1):1–12. https://doi.org/10.62411/jcta.10358
Funding
None.
Author information
Authors and Affiliations
Contributions
A.K. conceptualized the article and wrote the Methodology section, as well as contributed to the Original Draft Preparation and Visualization. N.K. supervised the project and analyzed the results based on the tables and figures. J.K. worked on the Methodology and Investigation sections and performed the model development tasks for the diagnosis and detection of various dental conditions. P.S.S prepared the results and performed the editing. All authors contributed equally to the proofreading.
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no conflict of interest.
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Kumar, A., Kumar, N., Kuriakose, J. et al. A deep transfer learning based approaches for the detection and classification of acute lymphocytic leukemia using microscopic images. Multimed Tools Appl 84, 30201–30225 (2025). https://doi.org/10.1007/s11042-024-20312-w
Received:
Revised:
Accepted:
Published:
Version of record:
Issue date:
DOI: https://doi.org/10.1007/s11042-024-20312-w