A Step Towards Automated Haematology: DL Models for Blood Cell Detection and Classification

Irfan Sadiq Rahat; Mohammed Altaf Ahmed; Donepudi Rohini; A Manjula; Hritwik Ghosh; Abdus Sobur

doi:10.4108/eetpht.10.5477

Authors

Irfan Sadiq Rahat Vellore Institute of Technology University
Mohammed Altaf Ahmed Prince Sattam Bin Abdulaziz University
Donepudi Rohini Koneru Lakshmaiah Education Foundation
A Manjula Jyothishmathi Institute of Technology and Science
Hritwik Ghosh Vellore Institute of Technology University
Abdus Sobur Westcliff University

DOI:

https://doi.org/10.4108/eetpht.10.5477

Keywords:

Medical Imaging, Diagnostic Methodologies, Blood Cell Classification, Hematology, ResNet50, AlexNet, MobileNetV2, VGG16, VGG19

Abstract

INTRODUCTION: Deep Learning has significantly impacted various domains, including medical imaging and diagnostics, by enabling accurate classification tasks. This research focuses on leveraging deep learning models to automate the classification of different blood cell types, thus advancing hematology practices.

OBJECTIVES: The primary objective of this study is to evaluate the performance of five deep learning models - ResNet50, AlexNet, MobileNetV2, VGG16, and VGG19 - in accurately discerning and classifying distinct blood cell categories: Eosinophils, Lymphocytes, Monocytes, and Neutrophils. The study aims to identify the most effective model for automating hematology processes.

METHODS: A comprehensive dataset containing approximately 8,500 augmented images of the four blood cell types is utilized for training and evaluation. The deep learning models undergo extensive training using this dataset. Performance assessment is conducted using various metrics including accuracy, precision, recall, and F1-score.

RESULTS: The VGG19 model emerges as the top performer, achieving an impressive accuracy of 99% with near-perfect precision and recall across all cell types. This indicates its robustness and effectiveness in automated blood cell classification tasks. Other models, while demonstrating competence, do not match the performance levels attained by VGG19.

CONCLUSION: This research underscores the potential of deep learning in automating and enhancing the accuracy of blood cell classification, thereby addressing the labor-intensive and error-prone nature of traditional methods in hematology. The superiority of the VGG19 model highlights its suitability for practical implementation in real-world scenarios. However, further investigation is warranted to comprehend model performance variations and ensure generalization to unseen data. Overall, this study serves as a crucial step towards broader applications of artificial intelligence in medical diagnostics, particularly in the realm of automated hematology, fostering advancements in healthcare technology.

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References

Islam, M. R., Nahiduzzaman, M., Goni, M. O. F., Sayeed, A., Anower, M. S., Ahsan, M., & Haider, J. (2022). Explainable Transformer-Based Deep Learning Model for the Detection of Malaria Parasites from Blood Cell Images. Sensors (Basel, Switzerland), 22(12), 4358. https://doi.org/10.3390/s22124358 DOI: https://doi.org/10.3390/s22124358

Mohamed, E., H. El-Behaidy, W., Khoriba, G., & Li, J. (2020). Improved White Blood Cells Classification based on Pre-trained Deep Learning Models. Journal of Communications Software and Systems, 16(1), 37–45. https://doi.org/10.24138/jcomss.v16i1.818 DOI: https://doi.org/10.24138/jcomss.v16i1.818

Dhieb, N., Ghazzai, H., Besbes, H., & Massoud, Y. (2019). An Automated Blood Cells Counting and Classification Framework using Mask R-CNN Deep Learning Model. 2019 31st International Conference on Microelectronics (ICM), 300–303. https://doi.org/10.1109/ICM48031.2019.9021862 DOI: https://doi.org/10.1109/ICM48031.2019.9021862

Habibzadeh, M., Jannesari, M., Rezaei, Z., Baharvand, H., & Totonchi, M. (2018). Automatic white blood cell classification using pre-trained deep learning models: ResNet and Inception. 10696, 1069612–1069612–8. https://doi.org/10.1117/12.2311282 DOI: https://doi.org/10.1117/12.2311282

Alzubaidi, L., Fadhel, M. A., Al-Shamma, O., Zhang, J., & Duan, Y. (2020). Deep Learning Models for Classification of Red Blood Cells in Microscopy Images to Aid in Sickle Cell Anemia Diagnosis. Electronics (Basel), 9(3), 427. https://doi.org/10.3390/electronics9030427 DOI: https://doi.org/10.3390/electronics9030427

Jha, K. K., & Dutta, H. S. (2019). Mutual Information based hybrid model and deep learning for Acute Lymphocytic Leukemia detection in single cell blood smear images. Computer Methods and Programs in Biomedicine, 179, 104987–104987. https://doi.org/10.1016/j.cmpb.2019.104987 DOI: https://doi.org/10.1016/j.cmpb.2019.104987

Deng, X., Shao, H., Shi, L., Wang, X., & Xie, T. (2020). A Classification–Detection Approach of COVID-19 Based on Chest X-ray and CT by Using Keras Pre-Trained Deep Learning Models. Computer Modeling in Engineering & Sciences, 125(2), 579.https://doi.org/10.32604/cmes.2020.011920 DOI: https://doi.org/10.32604/cmes.2020.011920

Anupama, C. S. S., Sivaram, M., Lydia, E. L., Gupta, D., & Shankar, K. (2022). Synergic deep learning model–based automated detection and classification of brain intracranial hemorrhage images in wearable networks. Personal and Ubiquitous Computing, 26(1), 1–10. https://doi.org/10.1007/s00779-020-01492-2 DOI: https://doi.org/10.1007/s00779-020-01492-2

D’Acunto, M., Martinelli, M., & Moroni, D. (2018). Deep Learning Approach to Human Osteosarcoma Cell Detection and Classification. Multimedia and Network Information Systems, 353–361. https://doi.org/10.1007/978-3-319-98678-4_36 DOI: https://doi.org/10.1007/978-3-319-98678-4_36

Sun, Y., Qu, Y., Wang, D., Li, Y., Ye, L., Du, J., Xu, B., Li, B., Li, X., Zhang, K., Shi, Y., Sun, R., Wang, Y., Long, R., Chen, D., Li, H., Wang, L., & Cao, M. (2021). Deep learning model improves radiologists' performance in detection and classification of breast lesions. Chinese Journal of Cancer Research, 33(6), 682–693. https://doi.org/10.21147/j.issn.1000-9604.2021.06.05 DOI: https://doi.org/10.21147/j.issn.1000-9604.2021.06.05

Marín, G., Casas, P., & Capdehourat, G. (2020). DeepMAL -- Deep Learning Models for Malware Traffic Detection and Classification. https://doi.org/10.48550/arxiv.2003.04079 DOI: https://doi.org/10.1007/978-3-658-32182-6_16

B R, P., Ashok, A., & A V, S. H. (2021). Plant Disease Detection and Classification Using Deep Learning Model. 2021 Third International Conference on Inventive Research in Computing Applications (ICIRCA), 1285–1291. https://doi.org/10.1109/ICIRCA51532.2021.9544729 DOI: https://doi.org/10.1109/ICIRCA51532.2021.9544729

Dulhare, U. N., & Mubeen, A. (2023). Detection and Classification of Rheumatoid Nodule using Deep Learning Models. Procedia Computer Science, 218, 2401–2410. https://doi.org/10.1016/j.procs.2023.01.215 DOI: https://doi.org/10.1016/j.procs.2023.01.215

Padma, T., Uday Kiran, A., Jahnavi, C., Rahul, S., Raja, N., & Kamal Kumar, M. (2022). Detection and Classification of Arrhythmias by Deploying Deep Learning Models. Journal of Physics. Conference Series, 2325(1), 12053. https://doi.org/10.1088/1742-6596/2325/1/012053 DOI: https://doi.org/10.1088/1742-6596/2325/1/012053

Madhu, M., Xavier, A., & Jayapandian, N. (2022). Covid-19 Classification and Detection Model using Deep Learning. 2022 International Conference on Electronics and Renewable Systems (ICEARS), 1457–1462. https://doi.org/10.1109/ICEARS53579.2022.9752290 DOI: https://doi.org/10.1109/ICEARS53579.2022.9752290

Ahmed, A. A., Jabbar, W. A., Sadiq, A. S., & Patel, H. (2022). Deep learning-based classification model for botnet attack detection. Journal of Ambient Intelligence and Humanized Computing, 13(7), 3457–3466. https://doi.org/10.1007/s12652-020-01848-9 DOI: https://doi.org/10.1007/s12652-020-01848-9

Researchers at Chitkara University Have Published New Study Findings on Computational Intelligence and Neuroscience (Deep Learning Model for the Automatic Classification of White Blood Cells). (2022). Obesity, Fitness, & Wellness Week, 3844.

Veeranampalayam Sivakumar, A. N., Li, J., Scott, S., Psota, E., J. Jhala, A., Luck, J. D., & Shi, Y. (2020). Comparison of Object Detection and Patch-Based Classification Deep Learning Models on Mid- to Late-Season Weed Detection in UAV Imagery. Remote Sensing (Basel, Switzerland), 12(13), 2136. https://doi.org/10.3390/rs12132136 DOI: https://doi.org/10.3390/rs12132136

Berganzo-Besga, I., Orengo, H. A., Lumbreras, F., Aliende, P., & Ramsey, M. N. (2022). Automated detection and classification of multi-cell Phytoliths using Deep Learning-Based Algorithms. Journal of Archaeological Science, 148, 105654. https://doi.org/10.1016/j.jas.2022.105654 DOI: https://doi.org/10.1016/j.jas.2022.105654

Wang, C.-W., Huang, S.-C., Lee, Y.-C., Shen, Y.-J., Meng, S.-I., & Gaol, J. L. (2022). Deep learning for bone marrow cell detection and classification on whole-slide images. Medical Image Analysis, 75, 102270–102270. https://doi.org/10.1016/j.media.2021.102270 DOI: https://doi.org/10.1016/j.media.2021.102270

Ghosh, H., Tusher, M.A., Rahat, I.S., Khasim, S., Mohanty, S.N. (2023). Water Quality Assessment Through Predictive Machine Learning. In: Intelligent Computing and Networking. IC-ICN 2023. Lecture Notes in Networks and Systems, vol 699. Springer, Singapore. https://doi.org/10.1007/978-981-99-3177-4_6 DOI: https://doi.org/10.1007/978-981-99-3177-4_6

Ghosh H, Rahat IS, Shaik K, Khasim S, Yesubabu M. Potato Leaf Disease Recognition and Prediction using Convolutional Neural Networks. EAI Endorsed Scal Inf Syst [Internet]. 2023 Sep. 21 [cited 2023 Sep. 22];.https://doi.org/10.4108/eetsis.3937 DOI: https://doi.org/10.4108/eetsis.3937

Rahat IS, Ghosh H, Shaik K, Khasim S, Rajaram G. Unraveling the Heterogeneity of Lower-Grade Gliomas: Deep Learning-Assisted Flair Segmentation and Genomic Analysis of Brain MR Images. EAI Endorsed Trans Perv Health Tech [Internet]. 2023 Sep. 29 [cited 2023 Oct. 2];9.https://doi.org/10.4108/eetpht.9.4016 DOI: https://doi.org/10.4108/eetpht.9.4016

M. Mandava, S. R. Vinta, H. Ghosh, and I. S. Rahat, “An All-Inclusive Machine Learning and Deep Learning Method for Forecasting Cardiovascular Disease in Bangladeshi Population”, EAI Endorsed Trans Perv Health Tech, vol. 9, Oct. 2023.https://doi.org/10.4108/eetpht.9.4052 DOI: https://doi.org/10.4108/eetpht.9.4052

Mohanty, S.N.; Ghosh, H.; Rahat, I.S.; Reddy, C.V.R. Advanced Deep Learning Models for Corn Leaf Disease Classification: A Field Study in Bangladesh. Eng. Proc. 2023, 59, 69. https://doi.org/10.3390/engproc2023059069 DOI: https://doi.org/10.3390/engproc2023059069

Md Abdus Shobur,Abdus Sobur,Md Ruhul Amin, "Walmart Data Analysis Using Machine Learning", International Journal of Creative Research Thoughts (IJCRT), ISSN:2320-2882, Volume.11, Issue 7, pp.f894-f898, July 2023, Available at :http://www.ijcrt.org/papers/IJCRT2307693

Nazrul Islam, Kazi and Sobur, Abdus and Kabir, Md Humayun, The Right to Life of Children and Cyberbullying Dominates Human Rights: Society Impacts (August 8, 2023). Available at SSRN: https://ssrn.com/abstract=4537139 DOI: https://doi.org/10.2139/ssrn.4537139

Md Humayun Kabir,Abdus Sobur,Md Ruhul Amin, "Stock Price Prediction Using the Machine Learning Model", International Journal of Creative Research Thoughts (IJCRT), ISSN:2320-2882, Volume.11, Issue 7, pp.f946-f950, July 2023, Available at :http://www.ijcrt.org/papers/IJCRT2307700

Md Suhel Rana, Md Humayun Kabir, & Abdus Sobur. (2023). Comparison of the Error Rates of MNIST Datasets Using Different Type of Machine Learning Model. https://doi.org/10.5281/zenodo.8010602