Recognition and Categorization of Blood Groups by Machine Learning and Image Processing Method

Authors

  • Mustafa F. Mahmood Department of Medical Instrumentation Techniques Engineering, Electrical Engineering Technical College, Middle Technical University, Iraq

DOI:

https://doi.org/10.20535/ibb.2024.8.2.298201

Keywords:

blood group classification, MATLAB image processing, Orange machine learning, processing time

Abstract

Background. Red blood cells are one of the components of blood. Blood is an important fluid in the human body. Knowing the blood groups is essential in blood transfusion operations, which depend on fixed conditions to avoid fatal errors. The method that is used to determine the blood groups is a traditional method that relies on medical laboratory technicians, as it is subject to human errors.

Objective. This paper aims to design and implement a prototype to detect and classify blood groups to avoid human error in blood group detection. The proposed system employs image processing and machine learning algorithms for blood group detection and classification.

Methods. The system consists of three stages. First, samples were collected from volunteers. Second, images of the samples were captured using a camera. Third, the images were analyzed using two methods: image proces­sing via MATLAB and machine learning algorithms via Orange, for blood group detection and classification.

Results. The accuracy in processing images using the MATLAB program reached 100%, with processing time ranged from 1.5 to 1.6 seconds. Additionally, using machine learning with neural networks in the Orange program, the accuracy was 99.7%, with training times of 13.7 seconds and testing times of 1.2 seconds. Neural networks outperformed other models, as shown in the experimental results. The study concluded that automated blood type detection using image processing and machine learning methods is effective and feasible compared to manual methods. The proposed system outperformed previous studies in terms of accuracy, processing time, training time, and testing time using both methods.

Conclusions. This study underscores the urgent need for precise blood type determination before emergency blood transfusions, which currently relies on manual inspection and is susceptible to human errors. These errors have the potential to endanger lives during blood transfusions. The main goal of the research was to develop an approach that combines image processing and machine learning to accurately classify blood groups.

References

Skull A. Circulation - anatomy, physiology and pathophysiology. In: McGloin S, Skull A. Principles of acute care nursing. SAGE Publications; 2022. p. 86.

Leslie HA, van Velzen MJM, Brandsma SH, Vethaak AD, Garcia-Vallejo JJ, Lamoree MH. Discovery and quantification of plastic particle pollution in human blood. Environ Int. 2022 May;163:107199. DOI: 10.1016/j.envint.2022.107199

Asadpour M, Olsen TL, Boyer O. An updated review on blood supply chain quantitative models: A disaster perspective. Transport Res Part E Logistics Transport Rev. 2022;158:102583. DOI: 10.1016/j.tre.2021.102583

Pei Q, Luo Y, Chen Y, Li J, Xie D, Ye T. Artificial intelligence in clinical applications for lung cancer: diagnosis, treatment and prognosis. Clin Chem Lab Med. 2022 Jun 30;60(12):1974-83. DOI: 10.1515/cclm-2022-0291

Sameer HA, Mutlag AH, Gharghan SK. CT-scan method-based artificial neural network for diagnosis of COVID-19. J Techniq. 2022;4(4):24-32. DOI: 10.51173/jt.v4i4.701

Ferraz A, Moreira V, Silva D, Carvalho VH, Soares FO. Automatic system for blood type classification using image processing techniques. In: BIODEVICES 2011 - Proceedings of the International Conference on Biomedical Electronics and Devices; 2011 Jan 26-29; Rome. p. 368-73. DOI: 10.5220/0003159003680373

Dhande A, Bhoir P, Gade V. Identifying the blood group using Image Processing. Int Res J Eng Technol. 2018;5:2639-42.

Rahman S, Rahman MA, Khan FA, Shahjahan SB, Nahar K. Blood group detection using image processing techniques. BRAC University; 2017. 37 p.

Vatshav S, Karthik K, Ch A, Kumar Patnaik MS. Determination and classification of blood types using image processing techniques. J Maharaja Sayajirao Univer Baroda. 2021;55(2):44-9.

Ravindran G, Joby T, Pravin M, Pandiyan P. Determination and classification of blood types using image processing techniques. Int J Comp Appl. 2017;157(1):12-6. DOI: 10.5120/ijca2017912592

Shaban S, Elsheweikh D. Blood group classification system based on image processing techniques. Intell Automat Soft Comput. 2022;31(2):817-34. DOI: 10.32604/iasc.2022.019500

Pavithra V, Rajeshwari J. Identification of blood group by using image processing techniques. J Eng Res Manag. 2019;3(5):90-5.

Yamin A, Imran F, Akbar U, Tanvir SH. Image processing based detection & classification of blood group using color images. In: Proceedings of 2017 International Conference on Communication, Computing and Digital Systems (C-CODE); 2017 Mar 8-9; Islamabad, Pakistan. Piscataway, NJ: IEEE; 2017. p. 293-8. DOI: 10.1109/C-CODE.2017.7918945

Jamil MMA, Oussama L, Hafizah WM, Wahab MHA, Johan MF. Computational automated system for red blood cell detection and segmentation. In: Intelligent data analysis for biomedical applications. Amsterdam: Elsevier; 2019. p. 173-89. DOI: 10.1016/B978-0-12-815553-0.00008-2

Sahastrabuddhe AP, Ajij S. Blood group detection and RBC, WBC counting: an image processing approach. Int J Eng Comput Sci. 2016;5(10):18635-9. DOI: 10.18535/ijecs/v5i10.49

Sathiyan S. Paul, Jennifer KS Glady, Swathi S, Sharmini G. Mariya. Blood group determination and classification using Raspberry Pi3. In: Proceedings of the International conference on microelectronics, signals and systems 2019. DOI: 10.1063/5.0003932

Ferraz A, Brito JH, Carvalho V, Machado J. Blood type classification using computer vision and machine learning. Neural Comput Appl. 2017;28(8):2029-40. DOI: 10.1007/s00521-015-2151-1

Gurav GV, Patil S. Automatic blood group classification based on SVM. Int J Sci Res Dev. 2017;5(8):317-21.

Raj Bhagat SM, Deshmukh P, Khanvilkar S. Automatic detection of human blood group system using deep learning and image processing. Int Res J Eng Technol. 2021;8(4):350-4.

Odeh N, Toma A, Mohammed F, Dama Y, Oshaibi F, Shaar M. An efficient system for automatic blood type determination based on image matching techniques. Appl Sci. 2021;11(11):5225. DOI: 10.3390/app11115225

Rosales MA, de Luna RG. Computer-based blood type identification using image processing and machine learning algorithm. J Adv Comput Intell Intell Inform. 2022;26(5):698-705. DOI: 10.20965/jaciii.2022.p0698

Vadgave AA, Vadgave AA, Ashtekar AS, Langade P. Determination of blood group using image processing. Int Res J Moderniz Eng Technol Sci. 2023;5(6):3172-8.

Dada A, Beck D, Schmitz G. Automation and data processing in blood banking using the Ortho AutoVue® Innova System. Transfus Med Hemother. 2007;34(5):341-6. DOI: 10.1159/000106558

Sharma S, Sharma H, Sharma JB. A new optimization based color image watermarking using non-negative matrix factorization in discrete cosine transform domain. J Ambient Intell Human Comput. 2022;13(9):4297-319. DOI: 10.1007/s12652-021-03408-1

Han ZY, Wang J, Fan H, Wang L, Zhang P. Unsupervised generative modeling using matrix product states. Phys Rev X. 2018 Jul;8(3):031012. DOI: 10.1103/PhysRevX.8.031012

Heriansyah R, Utomo WM. Performance evaluation of digital image processing by using Scilab. JUITA Jurnal Informatika. 2021;9(2):239-47. DOI: 10.30595/juita.v9i2.8434

Abdulazeez AM, Zeebaree DQ, Zebari DA, Hameed TH. Leaf identification based on shape, color, texture and vines using probabilistic neural network. Computación y Sistemas. 2021;25(3):617-31. DOI: 10.13053/cys-25-3-3470

Lynn N, Sourav A, Santoso A. Implementation of real-time edge detection using Canny and Sobel algorithms. In: IOP Conference Series: Materials Science and Engineering, Volume 1096, The 6th International Conference on Industrial, Mechanical, Electrical and Chemical Engineering; 2020 Oct 20; Solo, Indonesia. DOI: 10.1088/1757-899X/1096/1/012079

Makandar A, Kaman S, Biradar R, Javeriya SB. Impact of edge detection algorithms on different types of images using PSNR and MSE. LC Int J STEM. 2022;3(4):1-11.

Krestanova A, Kubicek J, Penhaker M, Timkovic J. Premature infant blood vessel segmentation of retinal images based on hybrid method for the determination of tortuosity. Lékař a technika-Clinician and Technology. 2020;50(2):49-57. DOI: 10.14311/CTJ.2020.2.02

Ullah B, Khan A, Fahad M, Alam M, Noor A, Saleem U, Kamran M. A novel approach to enhance dual-energy X-ray images using region of interest and discrete wavelet transform. J Inf Process Syst. 2022;18(3):319-31.

Li Y, Liu H, Tian Z, Geng W. Near-infrared vascular image segmentation using improved level set method. Infrared Phys Technol. 2023;131:104678. DOI: 10.1016/j.infrared.2023.104678

Wang L, Gu X, Liu Z, Wu W, Wang D. Automatic detection of asphalt pavement thickness: A method combining GPR images and improved Canny algorithm. Measurement. 2022;196:111248. DOI: 10.1016/j.measurement.2022.111248

Kowalczyk M, Ciarach P, Przewlocka-Rus D, Szolc H, Kryjak T. Real-time FPGA implementation of parallel connected component labelling for a 4K video stream. J Signal Process Syst. 2021;93(5):481-98. DOI: 10.1007/s11265-021-01636-4

Miikkulainen R, Liang J, Meyerson E, Rawal A, Fink D, Francon O, et al. Evolving deep neural networks. In: Kozma R, Alippi C, Choe Y, Morabito FC, editors. Artificial intelligence in the age of neural networks and brain computing. Elsevier; 2019. p. 293-312. DOI: 10.1016/B978-0-12-815480-9.00015-3

Burlina P, Paul W, Liu TYA, Bressler NM. Detecting anomalies in retinal diseases using generative, discriminative, and self-supervised deep learning. JAMA Ophthalmol. 2022;140(2):185-9. DOI: 10.1001/jamaophthalmol.2021.5557

Shaikh F, Rao D. Prediction of cancer disease using machine learning approach. Mater Today Proc. 2022;50:40-7. DOI: 10.1016/j.matpr.2021.03.625

Choudhary G, Singh SN. Prediction of heart disease using machine learning algorithms. In: Proceedings of 2020 International Conference on Smart Technologies in Computing, Electrical and Electronics; Bengaluru, India. p. 197-202. DOI: 10.1109/ICSTCEE49637.2020.9276802

Singh P. Logistic regression. In: Machine learning with PySpark: With natural language processing and recommender systems. Apress Berkeley; 2022. pp. 75-103. DOI: 10.1007/978-1-4842-7777-5_5

Zaidi A, Al Luhayb ASM. Two statistical approaches to justify the use of the logistic function in binary logistic regression. Math Probl Eng. 2023;2023(1):1-11. DOI: 10.1155/2023/5525675

Li X. Artificial intelligence neural network based on intelligent diagnosis. J Ambient Intell Human Comput. 2021;12:923-31. DOI: 10.1007/s12652-020-02108-6

Gholami V, Khaleghi MR, Pirasteh S, Booij MJ. Comparison of self-organizing map, artificial neural network, and co-active neuro-fuzzy inference system methods in simulating groundwater quality: Geospatial artificial intelligence. Water Resour Manag. 2022;36(2):451-69. DOI: 10.1007/s11269-021-02969-2

Sinha K, Uddin Z, Kawsar H, Islam S, Deen M, Howlader M. Analyzing chronic disease biomarkers using electrochemical sensors and artificial neural networks. TrAC Trends Anal Chem. 2023;158:116861. DOI: 10.1016/j.trac.2022.116861

Mustapha MT, Ozsahin DU, Ozsahin I, Uzun B. Breast cancer screening based on supervised learning and multi-criteria decision-making. Diagnostics. 2022;12(6):1326. DOI: 10.3390/diagnostics12061326

Li J, Huang Q, Ren S, Jiang L, Deng B, Qin Y. A novel medical text classification model with Kalman filter for clinical decision making. Biomed Signal Process Control. 2023;82:104503. DOI: 10.1016/j.bspc.2022.104503

Khotimah BK. Performance of the K-nearest neighbors method on identification of maize plant nutrients. J Infotel. 2022;14(1):8-14. DOI: 10.20895/infotel.v14i1.735

Uddin S, Haque I, Lu H, Moni MA, Gide E. Comparative performance analysis of K-nearest neighbour (KNN) algorithm and its different variants for disease prediction. Sci Rep. 2022;12(1):6256. DOI: 10.1038/s41598-022-10358-x

Jo T. Support vector machine. In: Machine learning foundations: supervised, unsupervised, and advanced learning. Springer Cham; 2021. pp. 167-88. DOI: 10.1007/978-3-030-65900-4_8

Montesinos López OA, Montesinos López A, Crossa J. Support vector machines and support vector regression. In: Multivariate statistical machine learning methods for genomic prediction. Springer, Cham; 2022:337-378. DOI: 10.1007/978-3-030-89010-0_9

Mahmood MF, Mohammed SL, Gharghan SK, Zubaidi SL. Wireless power transfer based on spiral-spider coils for a wireless heart rate sensor. In: Proceedings of 2020 13th International Conference on Developments in eSystems Engineering (DeSE); Liverpool, United Kingdom. pp. 183-8. DOI: 10.1109/DeSE51703.2020.9450775

Downloads

Published

2024-06-17

How to Cite

1.
Mahmood MF. Recognition and Categorization of Blood Groups by Machine Learning and Image Processing Method. Innov Biosyst Bioeng [Internet]. 2024Jun.17 [cited 2024Dec.10];8(2):53-68. Available from: https://ibb.kpi.ua/article/view/298201

Issue

Section

Articles