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Blood type classification using computer vision and machine learning

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Abstract

In emergency situations, where time for blood transfusion is reduced, the O negative blood type (the universal donor) is administrated. However, sometimes even the universal donor can cause transfusion reactions that can be fatal to the patient. As commercial systems do not allow fast results and are not suitable for emergency situations, this paper presents the steps considered for the development and validation of a prototype, able to determine blood type compatibilities, even in emergency situations. Thus it is possible, using the developed system, to administer a compatible blood type, since the first blood unit transfused. In order to increase the system’s reliability, this prototype uses different approaches to classify blood types, the first of which is based on Decision Trees and the second one based on support vector machines. The features used to evaluate these classifiers are the standard deviation values, histogram, Histogram of Oriented Gradients and fast Fourier transform, computed on different regions of interest. The main characteristics of the presented prototype are small size, lightweight, easy transportation, ease of use, fast results, high reliability and low cost. These features are perfectly suited for emergency scenarios, where the prototype is expected to be used.

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References

  1. Roback JD, Grossman BJ, Harris T, Hillyer CD (2011) Technical manual, 17th edn. American Association of Blood Banks

  2. Hammering DM (2012) Modern blood banking and transfusion practices, 6th edn. F.A. Davis Company

  3. Rod SR, Tate P, Trent DS (2005) Anatomia and fisiologia, 6th edn. Lusociência, Loures

    Google Scholar 

  4. Hoffbrand VA, Pettit EJ, Moss HAP (2004) Fundamentos em Hematologia, 4a Edição. Artmed, Porto Alegre

    Google Scholar 

  5. Caquet R (2004) Guia Prático Climepsi de Análises Clínicas, 1st ed. Climepsi Editores

  6. Blood Group Terminology (1935) ISBT—international society of blood transfusion. http://www.isbtweb.org/working-parties/red-cell-immunogenetics-and-blood-group-terminology/blood-group-terminology/

  7. U.S. Congress, Office of Technology Assessment, OTA-H-260 (1985) Blood policy and technology. Library of Congress Catalog Card Number 85-601151. U.S. Government Printing Office, Washington, DC

  8. Sturgeon P (2001) Automation: its introduction to the field of blood group serology. Immunohematology 17(4):100–105

    Google Scholar 

  9. Coakley AW (1981) Handbook of automated analysis. Mercel Dekker, New York

    Google Scholar 

  10. Ewing GW (1997) Analytical instrumentation handbook, 2nd edn. Marcel Dekker, New York, p 152

    Google Scholar 

  11. The Technicon Auto Analyzer II (2014). http://hydrology1.nmsu.edu/Teaching_Material/SOIL698/Student_Material/AutoAnalyzer/Autodiag.html

  12. Garretta M, Gener J, Muller A, Matte C, Moullec J (2000) The groupamatic system for routine immunohematology. Transfusion 15:422–431

    Article  Google Scholar 

  13. Zaccarelli GD, Monti G, Malaguti J, Marchesini D, Figliola F, Cagliari G, Basile C (2000) Esperienza di automazione nella determinazione dei gruppi sanguigni. La Trasfusione del Sangue 45:28–31

    Google Scholar 

  14. OLYMPUS TECHNOZONE Vol. 67 2006-11 (2011). http://www.olympus-global.com/en/magazine/techzone/vol67_e/page5.cfm

  15. Serodia-TPPA Auto (For In Vitro Diagnostic Use). http://www.mastgrp.com/Fuji/IFU/TPPAauto.pdf

  16. Formulated for use in Automated System Olympus® PK® Systems”—BLOOD GROUPING REAGENTS (2007). http://www.fda.gov/downloads/BiologicsBloodVaccines/BloodBloodProducts/ApprovedProducts/LicensedProductsBLAs/BloodDonorScreening/BloodGroupingReagent/ucm081307.pdf

  17. Immucor. http://immucor.com/site/aum_company_profile.jsp

  18. Wittmann G, Frank J, Schram W, Spannagl M (2007) Automation and data processing with the Immucor Galileo® System in a University Blood Bank. Transfus Med Hemother 34:347–352

    Article  Google Scholar 

  19. Dada A, Beck D, Schmitz G (2007) Automation and data processing in blood banking using the Ortho AutoVue® Innova System. Transfus Med Hemother 34:341–346

    Article  Google Scholar 

  20. Anti-Human Globulin, 2008. http://www.fda.gov/downloads/BiologicsBloodVaccines/BloodProducts/ApprovedProducts/LicensedProductsBLAs/BloodDonor/BloodGroupingReagent/ucm080763.pdf

  21. Shin SY, Kwon KC, Koo SH, Park JW, Ko CS, Song JH, Sung JY (2008) Evaluation of two automated instruments for pre-transfusion testing: AutoVueInnova and Techno TwinStation. Korean J Lab Med 3:214–220

    Article  Google Scholar 

  22. Roback JD, Combs MR, Grossman BJ, Hillyer CD (2008) Technical manual. AABB, Maryland

    Google Scholar 

  23. Murphy MM, Pamphilon DH (2009) Practical transfusion medicine, 3rd edn. Wiley-Blackwell, Oxford

    Book  Google Scholar 

  24. Cressiers/Morat (2008) Datasheet of Diamed Diaclon Anti-A, Diaclon Anti-B, Diaclon Anti-AB. http://diamedil.info/diamed/Inserts/100910.pdf

  25. Cressiers/Morat Datasheet of Diamed: Diamed-MP Test. Bio-Rad Laboratories, Inc. http://www.bio-rad.com/en-pt/product/diamed-mp-test-b-dvi-dvi-ctl-a1-a2-b

  26. Cressier M (2008) Datasheet of diamed-ID micro typing system, MTS gel card

  27. Inventor Professional. http://www.autodesk.com/education/free-software/inventor-professional

  28. Costa A Autodesk Inventor 2013 - Curso Completo. FCA

  29. Ferraz A, Carvalho V, Soares F (2013) A Prototype for blood typing based on image processing. In: SensorDevices 2013: the fourth international conference on sensor device technologies and applications, 2013

  30. Arduino Duemilanove. http://arduino.cc/en/Main/arduinoBoardDuemilanove

  31. Arduino Ethernet Shield. http://arduino.cc/en/Main/ArduinoEthernetShield

  32. Lehmann TM, Gonner C, Spitzer K (1999) Survey: interpolation methods in medical image processing. IEEE Trans Med Imaging 18(11):1049–1075

    Article  Google Scholar 

  33. Studholme C, Hill DLG, Hawkes DJ (1999) An overlap invariant entropy measure of 3D medical image alignment. Pattern Recognit 32(1):71–86

    Article  Google Scholar 

  34. Pluim JPW, Maintz JBA, Viergever MA (2003) Mutual-information-based registration of medical images: a survey. IEEE Trans Med Imaging 22(8):986–1004

    Article  MATH  Google Scholar 

  35. Boykov YY, Jolly M-P (2001) Interactive graph cuts for optimal boundary and region segmentation of objects in N-D images. In: Proceedings. eighth IEEE international conference on computer vision, 2001. ICCV 2001, pp 105–112

  36. Walter T, Klein JC, Massin P, Erginay A (2002) A contribution of image processing to the diagnosis of diabetic retinopathy–detection of exudates in color fundus images of the human retina. IEEE Trans Med Imaging 21(10):1236–1243

    Article  Google Scholar 

  37. Shattuck DW, Sandor-Leahy SR, Schaper KA, Rottenberg DA, Leahya RM (2001) Magnetic resonance image tissue classification using a partial volume model. Neuroimage 13(5):856–876

    Article  Google Scholar 

  38. Ferraz A, Carvalho V, Brandão P (2010) Automatic determination of human blood types using image processing techniques. In: BIODEVICES 2010 international conference on biomedical electronics and devices

  39. Ferraz A, Carvalho V, Soares F, Leão CP (2011) Characterization of blood samples using image processing techniques. Sens Actuators A Phys 172(1):308–314

    Article  Google Scholar 

  40. Moreira V, Ferraz A, Carvalho V, Soares F, Machado J (2012) Design of a mechatronic system for human blood typing in emergency situations. In: IEEE international conference on emerging technologies and factory automation, ETFA

  41. Bezerra K, Ferraz A, Carvalho V, Machado J, Matos J, Soares F (2012) Advanced design of a mechatronic system for human blood typing. Romanian Rev Precis Mech Opt Mechatron 41:144–150

    Google Scholar 

  42. Dalall N, Triggs B (2005) Histograms of oriented gradients for human detection. In: Conference on computer vision and pattern recognition (CVPR), vol 1, 2005, pp 886–893

  43. Pauker SG, Kassirer JP (1975) Therapeutic decision making: a cost-benefit analysis. N Engl J Med 293(5):216–221

    Article  Google Scholar 

  44. Lin R-H (2009) An intelligent model for liver disease diagnosis. Artif Intell Med 47(1):53–62

    Article  Google Scholar 

  45. Sheppard JW, Kaufman MA, Wilmer TJ (2009) IEEE standards for prognostics and health management. Aerosp Electron Syst Mag IEEE 24(9):34–41

    Article  Google Scholar 

  46. Lavrac N (1999) Selected techniques for data mining in medicine. Artif Intell Med 16(1):3–23

    Article  MathSciNet  Google Scholar 

  47. Huanga M-J, Chenb M-Y, Show-Chin L (2007) Integrating data mining with case-based reasoning for chronic diseases prognosis and diagnosis. Expert Syst Appl 32(3):856–867

    Article  Google Scholar 

  48. Kononenko I (2001) Machine learning for medical diagnosis: history, state of the art and perspective. Artif Intell Med 23(1):89–109

    Article  Google Scholar 

  49. Isern D, Sánchez D, Moreno A (2010) Agents applied in health care: a review. Int J Med Inform 79(3):145–166

    Article  Google Scholar 

  50. Rocha M, Cortez P, Neves JM Análise Inteligente de Dados, 1st ed. FCA

  51. Won Y, Song H-J, Kang TW, Kim J-J, And B-DH, Lee S (2003) Pattern analysis of serum proteome distinguishes renal cell carcinoma from other urologic diseases and healthy persons. Proteomics 3(12):2310–2316

    Article  Google Scholar 

  52. Freitas A, Costa-Pereira A, Brazdil P (2007) Cost-sensitive decision trees applied to medical data. Data Warehous Knowl Discov 4654:303–312

    Google Scholar 

  53. Aydin I, Karakose M, Akin E (2011) A multi-objective artificial immune algorithm for parameter optimization in support vector machine. Appl Soft Comput 11(1):120–129

    Article  Google Scholar 

  54. Chang C-L, Chen C-H (2009) Applying decision tree and neural network to increase quality of dermatologic diagnosis. Expert Syst Appl 36(2):4035–4041

    Article  Google Scholar 

  55. Barakat N, Bradley AP, Barakat MNH (2010) Intelligible support vector machines for diagnosis of diabetes mellitus. IEEE Trans Inf Technol Biomed 14(4):1114–1120

    Article  Google Scholar 

  56. Wu T-K, Huang S-C, Meng Y-R (2008) Evaluation of ANN and SVM classifiers as predictors to the diagnosis of students with learning disabilities. Expert Syst Appl 34(3):1846–1856

    Article  Google Scholar 

  57. Greer BT, Khan J (2004) Diagnostic classification of cancer using DNA microarrays and artificial intelligence. Appl Bioinform Cancer Detect 1020:46–66

    Google Scholar 

  58. Polat K, Güneş S (2007) Breast cancer diagnosis using least square support vector machine. Digit Signal Process 17(4):694–701

    Article  Google Scholar 

  59. Akay MF (2009) Support vector machines combined with feature selection for breast cancer diagnosis. Expert Syst Appl 36(2):3240–3247

    Article  Google Scholar 

  60. Widodo A, Yang B-S (2007) Support vector machine in machine condition monitoring and fault diagnosis. Mech Syst Signal Process 21(6):2560–2574

    Article  Google Scholar 

  61. Statistics Toolbox. http://www.mathworks.com/products/statistics/

  62. Matlab Software. The MathWorks, Inc. http://www.mathworks.com/products/matlab/

  63. Matlab - Machine Learning. The MathWorks, Inc. http://www.mathworks.com/machine-learning/

  64. Classification Trees and Regression Trees. http://www.mathworks.com/help/stats/classification-trees-and-regression-trees-1.html

  65. Support Vector Machines. http://www.mathworks.com/help/stats/support-vector-machines.html

  66. Hospital Prof. Doutor Fernando Afonseca, EPE. http://www.hff.min-saude.pt/

  67. Russel S, Norvig P (2009) Artificial intelligence: a modern approach, 3rd edn. Pearson, London

    Google Scholar 

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Acknowledgments

Authors of this paper want to thank Portuguese Foundation for Science and Technology (FCT) for funding through the PhD scholarship SFRH/BD/81094/2011. This work is funded also by FEDER funds through the “Programa Operacional Factores de Competitividade—COMPETE” and by national funds by FCTFundação para a Ciência e a Tecnologia, project reference PEst-UID/CEC/00319/2013.

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Authors and Affiliations

  1. R&D ALGORITMI Center, School of Engineering, University of Minho, 4800-058, Guimarães, Portugal

    Ana Ferraz & Vítor Carvalho

  2. MEtRICs Research Center, School of Engineering, University of Minho, 4800-058, Guimarães, Portugal

    Ana Ferraz & José Machado

  3. IPCA-EST, Polytechnic Institute of Cávado and Ave, 4750-810, Barcelos, Portugal

    José Henrique Brito & Vítor Carvalho

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  1. Ana Ferraz
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Correspondence to José Machado.

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Ferraz, A., Brito, J.H., Carvalho, V. et al. Blood type classification using computer vision and machine learning. Neural Comput & Applic 28, 2029–2040 (2017). https://doi.org/10.1007/s00521-015-2151-1

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