Research Article

Multiclass Blood Cell Classification Using Contourlet Transform and Metaheuristic-Optimized Deep Features with Clustering-Based Decision Making

Authors

Omid Eslamifar * Department of Electrical Engineering, Saveh Branch, Islamic Azad University, Saveh, Iran.
Mohammadreza Soltani Department of Electrical Engineering, Khomeinishahr Branch, Islamic Azad University, Khomeinishahr, Isfahan, Iran.
Seyed Mohammad Jalal Rastegar Fatemi Department of Electrical Engineering, Saveh Branch, Islamic Azad University, Saveh, Iran.

https://doi.org/10.22105/scfa.v1i4.58

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Abstract

Analysis by pathologists is time-consuming and error-prone due to the similarity among cell types. To address this, we propose a hybrid method combining deep learning and contourlet transform for multiclass blood cell classification. Features are optimized using a metaheuristic algorithm inspired by African vultures. Experimental results on the Jiangxi Tecom dataset demonstrate high performance, achieving classification accuracies of up to 97% for specific cell types. This approach improves diagnostic reliability by leveraging feature-level fusion and clustering-based decision making.

Keywords

White blood cell Classification Contourlet transform Recurrent neural network Precision

References (20)

[1] Alqahtani, A., Alsubai, S., Sha, M., Khan, M., Alhaisoni, M., & Naqvi, S. (2023). Automated white blood cell disease recognition using lightweight deep learning. Computer systems science and engineering, 46(1), 107–123. http://dx.doi.org/10.32604/csse.2023.030727

[2] Ramoser, H., Laurain, V., Bischof, H., & Ecker, R. (2005). Leukocyte segmentation and classification in blood-smear images. 2005 IEEE engineering in medicine and biology 27th annual conference (pp. 3371–3374). IEEE. https://doi.org/10.1109/IEMBS.2005.1617200

[3] Ghaderzadeh, M., Asadi, F., Hosseini, A., Bashash, D., Abolghasemi, H., & Roshanpour, A. (2021). Machine learning in detection and classification of leukemia using smear blood images: A systematic review. Scientific programming, 2021(1), 9933481. https://doi.org/10.1155/2021/9933481

[4] Harrison, C. J., & Johansson, B. (2015). Acute lymphoblastic leukemia. In Cancer cytogenetics: Chromosomal and molecular genetic aberrations of tumor cells (pp. 198–251). Wiley Online Library. https://doi.org/10.1002/9781118795569.ch10

[5] Simionato, G., Hinkelmann, K., Chachanidze, R., Bianchi, P., Fermo, E., van Wijk, R., … ., & Quint, S. (2021). Red blood cell phenotyping from 3D confocal images using artificial neural networks. Public library of science computational biology, 17(5), e1008934. https://doi.org/10.1371/journal.pcbi.1008934

[6] Mittal, A., Dhalla, S., Gupta, S., & Gupta, A. (2022). Automated analysis of blood smear images for leukemia detection: A comprehensive review. Association for computing machinery computing surveys (CSUR), 54(11s), 1–37. https://doi.org/10.1145/3514495

[7] Raji, H., Tayyab, M., Sui, J., Mahmoodi, S. R., & Javanmard, M. (2022). Biosensors and machine learning for enhanced detection, stratification, and classification of cells: A review. Biomedical microdevices, 24(3), 26. https://doi.org/10.1007/s10544-022-00627-x

[8] Yentrapragada, D. (2023). Deep features based convolutional neural network to detect and automatic classification of white blood cells. Journal of ambient intelligence and humanized computing, 14(7), 9191–9205. https://doi.org/10.1007/s12652-022-04422-7

[9] Niranjana, R., Ravi, A., Meena, A., Khaashwini, M. S., Kavya, T., & Krishnan, R. S. (2023). Blood cell counting and malaria pathogen detection using convolutional neural network. 2023 4th international conference on electronics and sustainable communication systems (ICESC) (pp. 1120–1127). IEEE. https://doi.org/10.1109/ICESC57686.2023.10193462

[10] Soltanzadeh, R., & Rabbani, H. (2010). Classification of three types of red blood cells in peripheral blood smear based on morphology. IEEE 10th international conference on signal processing proceedings (pp. 707–710). IEEE. https://doi.org/10.1109/ICOSP.2010.5655754

[11] Tiwari, P., Qian, J., Li, Q., Wang, B., Gupta, D., Khanna, A., … ., & de Albuquerque, V. H. C. (2018). Detection of subtype blood cells using deep learning. Cognitive systems research, 52, 1036–1044. https://doi.org/10.1016/j.cogsys.2018.08.022

[12] Bhavani, M., & Durgadevi, M. (2023). Streamlined classification of microscopic blood cell images. International journal of intelligent systems and applications in engineering, 11(1s), 57–62. https://www.ijisae.org/index.php/IJISAE/article/view/2477

[13] Elhassan, T. A., Mohd Rahim, M. S., Siti Zaiton, M. H., Swee, T. T., Alhaj, T. A., Ali, A., & Aljurf, M. (2023). Classification of atypical white blood cells in acute myeloid leukemia using a two-stage hybrid model based on deep convolutional autoencoder and deep convolutional neural network. Diagnostics, 13(2), 196. https://doi.org/10.3390/diagnostics13020196

[14] Ahmad, R., Awais, M., Kausar, N., & Akram, T. (2023). White blood cells classification using entropy-controlled deep features optimization. Diagnostics, 13(3), 352. https://doi.org/10.3390/diagnostics13030352

[15] Sallam, N. M., Saleh, A. I., Arafat Ali, H., & Abdelsalam, M. M. (2023). An efficient EGWO algorithm as feature selection for B-ALL diagnoses and its subtypes classification using peripheral blood smear images. Alexandria engineering journal, 68, 39–66. https://doi.org/10.1016/j.aej.2023.01.004

[16] Zheng, X., Tang, P., Ai, L., Liu, D., Zhang, Y., & Wang, B. (2023). White blood cell detection using saliency detection and CenterNet: A two-stage approach. Journal of biophotonics, 16(3), e202200174. https://doi.org/10.1002/jbio.202200174

[17] Salehi, R., Sadafi, A., Gruber, A., Lienemann, P., Navab, N., Albarqouni, S., & Marr, C. (2022). Unsupervised cross-domain feature extraction for single blood cell image classification. Medical image computing and computer assisted intervention - miccai 2022 (pp. 739–748). Cham: Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-16437-8_71

[18] Ghaderzadeh, M., Aria, M., Hosseini, A., Asadi, F., Bashash, D., & Abolghasemi, H. (2022). A fast and efficient CNN model for B-ALL diagnosis and its subtypes classification using peripheral blood smear images. International journal of intelligent systems, 37(8), 5113–5133. https://doi.org/10.1002/int.22753

[19] Hosseini, A., Eshraghi, M. A., Taami, T., Sadeghsalehi, H., Hoseinzadeh, Z., Ghaderzadeh, M., & Rafiee, M. (2023). A mobile application based on efficient lightweight CNN model for classification of B-ALL cancer from non-cancerous cells: A design and implementation study. Informatics in medicine unlocked, 39, 101244. https://doi.org/10.1016/j.imu.2023.101244

[20] Abdollahzadeh, B., Gharehchopogh, F. S., & Mirjalili, S. (2021). African vultures optimization algorithm: A new nature-inspired metaheuristic algorithm for global optimization problems. Computers & industrial engineering, 158, 107408. https://doi.org/10.1016/j.cie.2021.107408

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Published 2024-10-14
Issue Vol. 1 No. 4 (2024): Soft Computing Fusion with Applications
Section Articles
Document Type Research Article
Views 690
Downloads 265

How to Cite

Eslamifar, O. ., Soltani, M. ., & Rastegar Fatemi, S. M. J. . (2024). Multiclass Blood Cell Classification Using Contourlet Transform and Metaheuristic-Optimized Deep Features with Clustering-Based Decision Making. Soft Computing Fusion With Applications , 1(4), 186-198. https://doi.org/10.22105/scfa.v1i4.58

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