Generic placeholder image

Current Medical Imaging

Editor-in-Chief

ISSN (Print): 1573-4056
ISSN (Online): 1875-6603

Research Article

Modeling of the Acute Lymphoblastic Leukemia Detection by Convolutional Neural Networks (CNNs)

Author(s): Annal A. Albeeshi* and Hanan S. Alshanbari

Volume 19, Issue 7, 2023

Published on: 07 December, 2022

Article ID: e141022210032 Pages: 15

DOI: 10.2174/1573405619666221014113907

Price: $65

Abstract

Background: The techniques differed in many of the literature on the detection of Acute Lymphocytic Leukemia from the blood smear pictures, as the cases of infection in the world and the Kingdom of Saudi Arabia were increasing and the causes of this disease were not known, especially for children, which is a serious and fatal disease.

Objective: Through this work we seek to contribute to discover the blood cells affected by Acute Lymphocytic Leukem and to find an effective and fast method and to have the correct diagnosis as the time factor is important in the diagnosis and the initiation of treatment. which is based on one of the deep learning techniques that specialize in very deep networks, the use of one of the CNNs is VGG16.

Methods: Detection scheme is implemented by pre-processing, feature extraction, model building, fine tuning method, classification are executed. By using VGG16 pre-trained, and using SVM and MLP classification algorithms in Machine Learning.

Results: Our results are evaluated based on criteria, such as Accuracy, Precision, Recall, and F1-Score. The accuracy results for SVM classifier MLP of 77% accuracy at 0.001 learning rate and the accuracy for SVM classifier 75% at 0.005 learning rate. Whereas, the best accuracy result for VGG16 model was 92.27% at 0.003 learning rate. The best validation accuracy result was 85.62% at 0.001 learning rate.

Keywords: Cancer statistics, leukemia, ALL leukaemia, CNNs, VGG16, transfer learning, classification SVM & MLP, feature extraction.

[2]
National Toxicology Program and others. 14th report on carcinogens. 2016. Res Triangle Park Natl Toxicol Program, US DUSEPArtment Heal Hum Serv 2019.
[4]
Macmillan Cancer Support, Acute Lymphoblastic Leukaemia (ALL), Causes of leukaemia (leukemia)_4. Available from: https://www.macmillan.org.uk/cancer-information-and-support/leukaemia/acute-lymphoblastic-leukaemia-all Available from: https://www.macmillan.org.uk/cancer-information-and-support/leukaemia/causes
[5]
Sajana T, Maguluri LP, Syamala M, Usha Kumari C. Classification of leukemia patients with different clinical presentation of blood cells. Mater Today Proc 2020. [Epub Ahead of Print]
[http://dx.doi.org/10.1016/j.matpr.2020.10.619]
[6]
Anilkumar KK, Manoj VJ, Sagi TM. A survey on image segmentation of blood and bone marrow smear images with emphasis to automated detection of leukemia. Biocybern Biomed Eng 2020; 40(4): 1406-20.
[http://dx.doi.org/10.1016/j.bbe.2020.08.010]
[7]
Muntasa A, Yusuf M. Modeling of the acute lymphoblastic leukemia detection based on the principal object characteristics of the color image. Procedia Comput Sci 2019; 157: 87-98.
[http://dx.doi.org/10.1016/j.procs.2019.08.145]
[8]
[9]
Stephen P, Hunger MD, Charles G, Mullighan MD. Acute lymphoblastic leukemia in children 14th ed. N Engl J Med 2015; 373: 1541-52.
[http://dx.doi.org/10.1056/NEJMra1400972]
[10]
Kaye SA, Robison LL, Smithson WA, Gunderson P, King FL, Neglia JP. Maternal reproductive history and birth characteristics in childhood acute lymphoblastic leukemia. Cancer 1991; 68(6): 1351-5.
[http://dx.doi.org/10.1002/1097-0142(19910915)68:6<1351:AID-CNCR2820680627>3.0.CO;2-J] [PMID: 1873786]
[11]
Morimoto LM, Kwan ML, Deosaransingh K, et al. History of early childhood infections and acute lymphoblastic leukemia risk among children in a us integrated health-care system. Am J Epidemiol 2020; 189(10): 1076-85.
[http://dx.doi.org/10.1093/aje/kwaa062] [PMID: 32322901]
[12]
National cancer institute, adult acute lymphoblastic leukemia treatment. 2021 Available from: https://www.cancer.gov/types/leukemia/patient/adult-all-treatment-pdq
[13]
World Health Organization. Cancer today, Data visualization for exploring the global cancer burden in 2020. 2020. Available from: https://www.who.int/health-topics/cancer#tab=tab_2%7D,%5Curl%7B Available from: gco.iarc.fr/today/home
[14]
Bawazir A, Al-Zamel N, Amen A, Akiel MA, Alhawiti NM, Alshehri A. The burden of leukemia in the Kingdom of Saudi Arabia: 15 years period (1999-2013). BMC Cancer 2019; 19(1): 703.
[http://dx.doi.org/10.1186/s12885-019-5897-5] [PMID: 31315607]
[15]
Cireşan DC, Meier U, Gambardella LM, Schmidhuber J. Deep, big, simple neural nets for handwritten digit recognition. Neural Comput 2010; 22(12): 3207-20.
[http://dx.doi.org/10.1162/NECO_a_00052] [PMID: 20858131]
[16]
Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition 3rd Int Conf Learn Represent ICLR 2015 - Conf Track Proc. 1-14.
[17]
Shahin AI, Guo Y, Amin KM, Sharawi AA. White blood cells identification system based on convolutional deep neural learning networks. Comput Methods Programs Biomed 2019; 168: 69-80.
[http://dx.doi.org/10.1016/j.cmpb.2017.11.015] [PMID: 29173802]
[18]
Krizhevsky A, Sutskever I, Hinton GE. ImageNet classification with deep convolutional neural networks. Adv Neural Inf Process Syst 2012; 2: 25.
[19]
Suah FBM. Preparation and characterization of a novel Co(II) optode based on polymer inclusion membrane. Anal Chem Res 2017; 12: 40-6.
[http://dx.doi.org/10.1016/j.ancr.2017.02.001]
[20]
Sermanet P, Eigen D, Zhang X, Mathieu M, Fergus R, Le Cun Y. Overfeat: Integrated recognition, localization and detection using convolutional networks 2nd Int Conf Learn Represent ICLR 2014 - Conf Track Proc.
[21]
Zeng G, He Y, Yu Z, Yang X, Yang R, Zhang L. Preparation of novel high copper ions removal membranes by embedding organosilane-functionalized multi-walled carbon nanotube. J Chem Technol Biotechnol 2016; 91(8): 2322-30.
[http://dx.doi.org/10.1002/jctb.4820]
[22]
Mallick PK, Mohapatra SK, Chae GS, Mohanty MN. Convergent learning-based model for leukemia classification from gene expression. Pers Ubiquitous Comput 2020; 1-8.
[http://dx.doi.org/10.1007/s00779-020-01467-3] [PMID: 33100943]
[23]
Vogado LHS, Veras RMS, Araujo FHD, Silva RRV, Aires KRT. Leukemia diagnosis in blood slides using transfer learning in CNNs and SVM for classification. Eng Appl Artif Intell 2018; 72: 415-22.
[http://dx.doi.org/10.1016/j.engappai.2018.04.024]
[24]
Nengliang O, Weijia W, Li M, et al. Diagnosing acute promyelocytic leukemia by using convolutional neural network. Clin Chim Acta 2020; 512: 1-6.
[http://dx.doi.org/10.1016/j.cca.2020.10.039]
[25]
Liao Q, Ding Y, Jiang ZL, Wang X, Zhang C, Zhang Q. Multi-task deep convolutional neural network for cancer diagnosis. Neurocomputing 2019; 348: 66-73.
[http://dx.doi.org/10.1016/j.neucom.2018.06.084]
[26]
Joshi SRS, Minal D, Atul HK. White blood cells segmentation and classification to detect acute leukemia. Int J Emerg Trends Technol Comput Sci 2013; 2(3): 147-51.
[27]
Singhal V, Singh P. Local binary pattern for automatic detection of acute lymphoblastic leukemia. 2014 20th Natl Conf Commun NCC; 2014 February- March 28- 02; Rohtak, India: IEEE 2014.
[http://dx.doi.org/10.1109/NCC.2014.6811261]
[28]
Khandekar R, Shastry P, Jaishankar S, Faust O, Sampathila N. Automated blast cell detection for acute lymphoblastic leukemia diagnosis. Biomed Signal Process Control 2021; 68(May): 102690.
[http://dx.doi.org/10.1016/j.bspc.2021.102690]
[29]
Rezatofighi SH, Soltanian-Zadeh H. Automatic recognition of five types of white blood cells in peripheral blood. Comput Med Imaging Graph 2011; 35(4): 333-43.
[http://dx.doi.org/10.1016/j.compmedimag.2011.01.003] [PMID: 21300521]
[30]
[31]
Kumar G, Bhatia PK. A detailed review of feature extraction in image processing systems Int Conf Adv Comput Commun Technol ACCT; 2014 February 08-09; Rohtak, India: IEEE 2014.
[http://dx.doi.org/10.1109/ACCT.2014.74]
[32]
ImageNet database. Available from: https://image-net.org/
[33]
Zhang X, Zou J, He K, Sun J. Accelerating very deep convolutional networks for classification and detection. IEEE Trans Pattern Anal Mach Intell 2016; 38(10): 1943-55.
[http://dx.doi.org/10.1109/TPAMI.2015.2502579] [PMID: 26599615]
[34]
Pang S, Yu Z, Orgun MA. A novel end-to-end classifier using domain transferred deep convolutional neural networks for biomedical images. Comput Methods Programs Biomed 2017; 140: 283-93.
[http://dx.doi.org/10.1016/j.cmpb.2016.12.019] [PMID: 28254085]
[35]
Kilicarslan S, Adem K, Celik M. Diagnosis and classification of cancer using hybrid model based on relief and convolutional neural network. Med Hypotheses 2020; 137: 109577.
[http://dx.doi.org/10.1016/j.mehy.2020.109577]
[36]
Kashef A, Khatibi T, Mehrvar A. Treatment outcome classification of pediatric acute lymphoblastic leukemia patients with clinical and medical data using machine learning: A case study at MAHAK hospital. Inform Med Unlocked 2020; 20: 100399.
[http://dx.doi.org/10.1016/j.imu.2020.100399]
[37]
Mishra S, Majhi B, Sa PK. Texture feature based classification on microscopic blood smear for acute lymphoblastic leukemia detection. Biomed Signal Process Control 2019; 47: 303-11.
[http://dx.doi.org/10.1016/j.bspc.2018.08.012]
[38]
Kingma DP, Lei Ba J. 15iclr-ADAM Iclr 2015; 1-15. Available from: https://arxiv.org/pdf/1412.6980.pdf
[39]
Fukami K, Fukagata K, Taira K. Assessment of supervised machine learning methods for fluid flows. Theor Comput Fluid Dyn 2020; 34(4): 497-519.
[http://dx.doi.org/10.1007/s00162-020-00518-y]
[40]
Davis JC, Wistinghausen B. Acute lymphoblastic leukemia. Oncology 2019; 381(9881): 319-31.
[http://dx.doi.org/10.1002/9781119189596.ch28]

Rights & Permissions Print Cite
© 2024 Bentham Science Publishers | Privacy Policy