Detection and classification of abnormal red blood cells with computational intelligence techniques: a review

Fernanda D'Amico Silva

ORCID iD Pontifícia Universidade Católica do Paraná (PUC-PR) Brasil

Evair Borges Severo

Hilab Brasil

Victor Henrique Alves Ribeiro

ORCID iD Silex Brasil

Gilberto Reynoso-Meza

ORCID iD Pontifícia Universidade Católica do Paraná (PUC-PR) Brasil


Red blood cells (RBCs), or erythrocytes, are usually disc-shaped. However, pathological conditions can change their shape. The complete blood count (CBC) is a test that can detect abnormal RBCs. Yet it is a manual test susceptible to errors, so there are efforts to automate the detection and classification of abnormal cells. A total of 31 papers were reviewed, and all the selected studies focus on the detection or classification of abnormal RBCs. Different approaches were applied to tackle this issue, including image processing techniques, classification using machine learning, and convolutional neural networks for detection, among other methods. Furthermore, machine learning techniques are recently presenting promising results for abnormal RBCs detection and classification. This review also brings a discussion on the computational intelligence methodologies applied. 


Abnormal cells classification; Computational intelligence; Machine learning; Red blood cells

Texto completo:


ABDULKARIM, H. A.; SUDIRMAN, R.; RAZAK, M. A. A. Normal and abnormal red blood cell recognition using image processing. Indonesian Journal of Electrical Engineering and Computer Science, v. 14, n. 1, p. 100-104, 2019. DOI:

ACHARJEE, S.; CHAKRABARTTY, S.; ALAM, M. I.; DEY, N.; SANTHI, V.; ASHOUR, A. S. A semiautomated approach using GUI for the detection of red blood cells. In: 2016 INTERNATIONAL CONFERENCE ON ELECTRICAL, ELECTRONICS, AND OPTIMIZATION TECHNIQUES (ICEEOT), 2016, Chennai. Proceedings… Chennai: IEEE, 2016, p. 525-529. DOI:

ACHARYA, V.; KUMAR, P. Identification and red blood cell classification using computer aided system to diagnose blood disorders. In: 2017 INTERNATIONAL CONFERENCE ON ADVANCES IN COMPUTING, COMMUNICATIONS AND INFORMATICS (ICACCI), 2017, Udupi. Proceedings… Udupi: IEEE, 2017, p. 2098-2104. DOI:

AKRIMI, J. A.; SULIMAN, A.; GEORGE, L. E.; AHMAD, A. R. Classification red blood cells using support vector machine. In: INTERNATIONAL CONFERENCE ON INFORMATION TECHNOLOGY AND MULTIMEDIA, 6., 2014, Putrajaya. Proceedings… Putrajaya: IEEE, p. 265-269, 2014. DOI:

ALAM, M. M.; ISLAM, M. T. Machine learning approach of automatic identification and counting of blood cells. Healthcare Technology Letters, v. 6, n. 4, p. 103-108, 2019. DOI:

ALZUBAIDI, L.; FADHEL, M. A.; AL-SHAMMA, O.; ZHANG, J.; DUAN, Y. Deep learning models for classification of red blood cells in microscopy images to aid in sickle cell anemia diagnosis. Electronics, v. 9, n. 3, p. 427, 2020. DOI:

BAI, M.; URTASUN, R. Deep watershed transform for instance segmentation. In: CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR), 2017, Honolulu. Proceedings… Honolulu: IEEE, 2017, p. 5221-5229. DOI:

BALARAJAN, Y.; RAMAKRISHNAN, U.; OZALTIN, E.; SHANKAR, A. H.; SUBRAMANIAN, S. V. Anaemia in low-income and middle-income countries. The Lancet, v. 378, n. 9809, p. 2123-2135, 2011. DOI:

BATITIS, V. M. E.; CABALLES, M. J. G.; CIUDAD, A. A.; DIAZ, M. D.; FLORES, R. D.; TOLENTIN, E. R. E.; Image classification of abnormal red blood cells using decision tree algorithm. In: 2020 INTERNATIONAL CONFERENCE ON COMPUTING METHODOLOGIES AND COMMUNICATION (ICCMC), 4., 2020, Erode. Proceedings… Erode: IEEE, 2020. p. 498-504. DOI:

BOHANEC, M.; RAJKOVIC, V. DEX: an expert system for decision support. Sistemica, v. 1, n. 1, p. 145-157, 1990. Available at: Accessed on: 4 May. 2022.

CHY, T. S.; RAHAMAN, M. A. Automatic sickle cell anemia detection using image processing technique. In: 2018 INTERNATIONAL CONFERENCE ON ADVANCEMENT IN ELECTRICAL AND ELECTRONIC ENGINEERING (ICAEEE), 2018, Gazipur. Proceedings… Gazipur: IEEE, 2018, p. 1-4. DOI:

CHY, T. S.; RAHAMAN, M. A. A comparative analysis by KNN, SVM & ELM classification to detect sickle cell anemia. In: 2019 INTERNATIONAL CONFERENCE ON ROBOTICS, ELECTRICAL AND SIGNAL PROCESSING TECHNIQUES. (ICREST), 2019, Dhaka. Proceedings… Dhaka: IEEE, 2019, p. 455-459. DOI:

DALVI, P. T.; VERNEKAR, N. Computer aided detection of abnormal red blood cells. In: 2016 IEEE INTERNATIONAL CONFERENCE ON RECENT TRENDS IN ELECTRONICS, INFORMATION & COMMUNICATION TECHNOLOGY (RTEICT), 2016, Bangalore. Proceedings… Bangalore: IEEE, 2016. p. 1741-1746. DOI:

DURANT, T. J. S.; OLSON, E. M.; SCHULZ, W. L.; TORRES, R. Very deep convolutional neural networks for morphologic classification of erythrocytes. Clinical Chemistry, v. 63, n. 12, p. 1847-1855, 2017. DOI:

ELSALAMONY, H. A. Healthy and unhealthy red blood cell detection in human blood smears using neural networks. Micron, v. 83, p. 32-41, 2016. DOI:

ELSALAMONY, H. A. Anaemia cells detection based on shape signature using neural networks. Measurement, v. 104, p. 50-59, 2017. DOI:

FADHEL, M. A.; HUMAIDI, A. J.; OLEIWI, S. R. Image processing-based diagnosis of sickle cell anemia in erythrocytes. In: 2017 ANNUAL CONFERENCE ON NEW TRENDS IN INFORMATION & COMMUNICATIONS TECHNOLOGY APPLICATIONS (NTICT), 2017, Baghdad. Proceedings… Baghdad: IEEE, 2017. p. 203-207. DOI:

GONZÁLEZ-HIDALGO, M.; GUERRERO-PENA, F. A.; HEROLD-GARCÍA, S.; JAUME-I-CAPÓ, A.; MARRERO-FERNÁNDEZ, P. D. Red blood cell cluster separation from digital images for use in sickle cell disease. IEEE Journal of Biomedical and Health Informatics, v. 19, n. 4, p. 1514-1525, 2014. DOI:

HABIBZADEH, M.; KRZYZAK, A.; FEVENS, T. White blood cell differential counts using convolutional neural networks for low resolution images. In: RUTKOWSKI, L.; KORYTKOWSKI, M.; SCHERER, R.; TADEUSIEWICZ, R.; ZADEH, L. A.; ZURADA, J. M. (eds). Artificial Intelligence and Soft Computing (ICAISC 2013). Lecture Notes in Computer Science, v, 7895; Berlin: Springer, 2013. DOI:

HORTINELA, C. C.; BALBIN, J. R.; FAUSTO, J. C.; DIVINA, P. D. C.; FELICES, J. T. F. Identification of abnormal red blood cells and diagnosing specific types of anemia using image processing and support vector machine. In: 2019 IEEE INTERNATIONAL CONFERENCE ON HUMANOID, NANOTECHNOLOGY, INFORMATION TECHNOLOGY, COMMUNICATION AND CONTROL, ENVIRONMENT, AND MANAGEMENT (HNICEM), 11., 2019, Laoag. Proceedings… Laoag: IEEE, 2019, p. 1-6. DOI:

LEE, H.; CHEN, Y.- P. P. Cell morphology-based classification for red cells in blood smear images. Pattern Recognition Letters, v. 49, p. 155-161, 2014. DOI:

KAJÁNEK, F.; CIMRÁK, I. Evaluation of detection of red blood cells using convolutional neural networks. In: 2019 INTERNATIONAL CONFERENCE ON INFORMATION AND DIGITAL TECHNOLOGIES (IDT), 2019, Zilina. Proceedings… Zilina: IEEE, p. 198-202, 2019. DOI:

KANNADAGULI, P. Microscopic blood smear RBC classification using PCA and SVM based Machine Learning. In: 2020 INTERNATIONAL CONFERENCE ON MULTIMIDIA PROCESSING, COMMUNICATION & INFORMATION TECHNOLOGY (MPCIT), 3., 2020, Shivamogga. Proceedings… Shivamogga: IEEE, 2020. p. 82-86. DOI:

LECUN, Y.; BENGIO, Y.; HINTON, G. Deep learning. Nature, v. 521, n. 7553, p. 436-444, 2015. DOI:

LOU, J.; ZHOU, M.; LI, Q.; YUAN, C.; LIU, H. An automatic red blood cell counting method based on spectral images. In: INTERNATIONAL CONGRESS ON IMAGE AND SIGNAL PROCESSING, BIOMEDICAL ENGINEERING AND INFORMATICS (CISP-BMEI), 9., 2016, Datong. Proceedings… Datong: IEEE, p. 1391-1396, 2016 DOI:

LOTFI, M.; NAZARI, B.; SADRI, S.; SICHANI, N. K. The detection of dacrocyte, schistocyte and elliptocyte cells in iron deficiency anemia. In: 2015 INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION AND IMAGE ANALYSIS (IPRIA), 2., 2015, Rasht. Proceedings… Rasht: IEEE, 2015, p. 1-5. DOI:

MAITY, M.; MUNGLE, T.; DHANE, D.; MAITI, A. K.; CHAKRABORTY, C. An ensemble rule learning approach for automated morphological classification of erythrocytes. Journal of Medical Systems, v. 41, n. 4, p. 56, 2017. DOI:

MITCHELL, T. M.; Machine learning. New York: McGraw-Hill, 1997.

MOLINA-CABELLO, M. A.; LÓPEZ-RUBIO, E.; LUQUE-BAENA, R. M.; RODRÍGUEZ-ESPINOSA, M. J.; THURNHOFER-HEMSI, K. Blood cell classification using the hough transform and convolutional neural networks. In: WORLD CONFERENCE ON INFORMATION SYSTEMS AND TECHNOLOGIES, 2018, Cham. Proceedings… Springer, p. 669-678. DOI:

PARAB, M. A.; MEHENDALE, N. D. Red blood cell classification using image processing and CNN. SN Computer Science, v. 2, 2021. DOI:

PARK, Y.; BEST, C. A.; BADIZADEGAN, K.; DASARI, R. R.; FELD, M. S.; KURIABOVA, T.; HENLE, M. L.; LEVINE, A. J.; POPESCU, G. Measurement of red blood cell mechanics during morphological changes. PNAS, v. 107, n. 15, p. 6731-6736, 2010. DOI:

PEDERSEN, S. J. K. Circular hough transform. Aalborg University, Vision, Graphics, and Interactive Systems, v. 123, n. 6, 2007. Available at: Accessed on: 4 may. 2022.

QIU, W.; GUO, J.; LI, X.; XU, M.; ZHANG, M.; GUO, N.; LI, Q. Multi-label detection and classification of red blood cells in microscopic images. In: 2020 IEEE INTERNATIONAL CONFERENCE ON BIG DATA (BIG DATA), 2020, Atlanta. Proceedings… Atlanta: IEEE, 2020, p. 4257-4263. DOI:

RAHMAN, S.; AZAM, B.; KHAN, S. U.; AWAIS, M.; ALI, I.; KHAN, R. J. H. Automatic identification of abnormal blood smear images using color and morphology variation of RBCS and central pallor. Computerized Medical Imaging and Graphics, v. 87, p. 101813, 2021. DOI:

RAKSHIT, P.; BHOWMIK, K. Detection of abnormal findings in human RBC in diagnosing sickle cell anaemia using image processing. Procedia Technology, v. 10, p. 28-36, 2013. DOI:

RAYAPPAN, L.; KARTHIK, R. Deep learning approach to the normal and abnormal blood cells in human blood smear. Annals of the Romanian Society for Cell Biology, v. 25, n. 2, p. 3187-3196, 2021. Available at: Accessed on: 4 may. 2022.

RAZZAK, M. I.; NAZ, S. Microscopic blood smear segmentation and classification using deep contour aware CNN and extreme machine learning. In: CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION WORKSHOPS (CVPRW), 2017, Honolulu. Proceedings… Honolulu: IEEE, 2017, p. 49-55. DOI:

RIBEIRO, V. H. A.; REYNOSO-MEZA, G. Ensemble learning by means of a multi-objective optimization design approach for dealing with imbalanced data sets. Expert Systems with Applications, v. 147, p. 113232, 2020. DOI:

SAFCA, N.; POPESCU, D.; ICHIM, L.; ELKHATIB, H.; CHENARU, O. Image processing techniques to identify red blood cells. In: INTERNATIONAL CONFERENCE ON SYSTEM THEORY, CONTROL AND COMPUTING (ICSTCC), 22., 2018, Sinaia. Proceedings… Sinaia: IEEE, p. 93-98, DOI:

SARRAFZADEH, O.; DEHNAVI, A. M.; RABBANI, H.; GHANE, N.; TALEBI, A. Circlet based framework for red blood cells segmentation and counting. In: 2015 IEEE WORKSHOP ON SIGNAL PROCESSING SYSTEMS (SiPS), 2015, Hangzhou. Proceedings… Hangzhou: IEEE, p. 1-6. 2015. DOI:

SEN, B.; GANESH, A.; BHAN, A.; DIXIT, S.; GOYAL, A. Machine learning based diagnosis and classification of sickle cell anemia in human RBC. In: 2021 INTERNATIONAL CONFERENCE ON INTELLIGENT COMMUNICATION TECHNOLOGIES AND VIRTUAL MOBILE NETWORKS, 3., 2021, Tirunelveli. Proceedings… Tirunelveli: IEEE, 2021, p. 753-758. DOI:

SHARMA, V.; RATHORE, A.; VYAS, G. Detection of sickle cell anaemia and thalassaemia causing abnormalities in thin smear of human blood sample using image processing. In: 2016 INTERNATIONAL CONFERENCE ON INVENTIVE COMPUTATION TECHNOLOGIES (ICICT), 2016, Coimbatore. Proceedings… Coimbatore: IEEE, p. 1-5, DOI:

SYAHPUTRA, M. F.; SARI, A. R.; RAHMAT, R. F. Abnormality classification on the shape of red blood cells using radial basis function network. In: 2017 INTERNATIONAL CONFERENCE ON COMPUTER APPLICATION AND INFORMATION PROCESSING TECHNOLOGY (CAIPT), 4., 2017, Kuta Bali. Proceedings… Kuta Bali: IEEE, 2017, p. 1-5. DOI:

TOHKA, J.; VAN GILS, M. Evaluation of machine learning algorithms for health and wellness applications: a tutorial. Computers in Biology and Medicine, v. 132, p. 104324, 2021. DOI:

TOMARI, R.; ZAKARIA, W. N. W.; JAMIL, M. M. A.; NOR, F. M.; FUAD, N. F. N. Computer aided system for red blood cell classification in blood smear image. Procedia Computer Science, v. 42, p. 206-213, 2014. DOI:

TYAGI, M.; SAINI, L. M.; DAHYIA, N. Detection of poikilocyte cells in iron deficiency anaemia using artificial neural network. In: 2016 INTERNATIONAL CONFERENCE ON COMPUTATION OF POWER, ENERGY INFORMATION AND COMMUNICATION (ICCPEIC), 2016, Melmaruvathur. Proceedings… Melmaruvathur: IEEE, 2016, p. 108-112. DOI:

TYAS, D. A.; RATNANINGSIH, T.; HARJOKO, A.; HARTATI, S. The classification of abnormal red blood cell on the minor thalassemia case using artificial neural network and convolutional neural network. In: INTERNATIONAL CONFERENCE ON VIDEO AND IMAGE PROCESSING (ICVIP 2017), 2017. Proceedings… p. 228-233, 2017. DOI:

WALTER, W.; HAFERLACH, C.; NADARAJAH, N.; SCHMIDTS, I.; KUHN, C.; KERN, W.; HAFERLACH, T. How artificial intelligence might disrupt diagnostics in hematology in the near future. Oncogene, v. 40, p. 1-10, 2021. DOI:

XU, M.; PAPAGEORGIOU, D. P.; ABIDI, S. Z.; DAO, M.; ZHAO, H.; KARNIADAKIS, G. E. A deep convolutional neural network for classification of red blood cells in sickle cell anemia. PLoS Computational Biology, v. 13, n. 10, p. e1005746, 2017. DOI:

YUNINGSIH, N.; MUSTIKASARI, M. Anemia classification based on abnormal red blood cell morphology using convolutional neural network. IOSR Journal of Computer Engineering (IOSR-JCE), v. 22, n., 1, p. 22-29, 2020. DOI:

ZHAO, J.; ZHANG, M.; ZHOU, Z.; CHU, J.; CAO, F. Automatic detection and classification of leukocytes using convolutional neural networks. Medical & Biological Engineering & Computing, v. 55, p. 1287-1301, 2017. DOI:


O arquivo PDF selecionado deve ser carregado no navegador caso tenha instalado um plugin de leitura de arquivos PDF (por exemplo, uma versão atual do Adobe Acrobat Reader).

Como alternativa, pode-se baixar o arquivo PDF para o computador, de onde poderá abrí-lo com o leitor PDF de sua preferência. Para baixar o PDF, clique no link abaixo.

Caso deseje mais informações sobre como imprimir, salvar e trabalhar com PDFs, a Highwire Press oferece uma página de Perguntas Frequentes sobre PDFs bastante útil.

Visitas a este artigo: 35

Total de downloads do artigo: 17