Author(s):
Khushi Shah, Kanika Nadar, Prashasti Kanikar
Email(s):
khushijs02@gmail.com
DOI:
10.52711/0974-360X.2025.00108 
Address:
Khushi Shah*, Kanika Nadar, Prashasti Kanikar
Department of Computer Engineering, Mukesh Patel School of Technology Management and Engineering, NMIMS University, Vile Parle West, Mumbai 400056, Maharashtra, India.
*Corresponding Author
Published In:
Volume - 18,
Issue - 2,
Year - 2025
ABSTRACT:
The manual classification of bone marrow (BM) cell morphology, a pivotal aspect of haematological diagnosis, is performed thousands of times daily due to the lack of comprehensive data sets and trained models. This process is often time-consuming and susceptible to human errors. The effectiveness of deep learning algorithms in biomedical applications is proven. The impact is undeniable and unquestionable as these techniques use extensive datasets encompassing diverse disease classes, meticulously annotated by medical professionals. This also eliminates any scope for error, shortens the diagnosis time and enhances the accuracy. Over time, deep learning techniques have improved further with new advancements. This research aims to evaluate the performance of several pretrained Convolutional Neural Network models to automate the classification of BM cells. More specifically, the models are compared by their ability to assist medical professionals in identifying the presence of ‘hairy cells’ in the bone marrow smear of a subject. The presence of hairy cells in the blood is indicative of the possibility of a person having Hairy Cell Leukemia. A custom CNN architecture, ConvNeXtSmall, ConvNeXtTiny, DenseNet121, EfficientNetB5, ResNet50, VGG16 and Xception were compared. ConvNeXtSmall has the highest validation accuracy of 0.85. DenseNet121 and ConvNeXtSmall have the highest F1 score of 0.81 while classifying Hairy Cells.
Cite this article:
Khushi Shah, Kanika Nadar, Prashasti Kanikar. Automated Classification of Cells from Bone Marrow Cytology with Deep Learning. Research Journal of Pharmacy and Technology.2025;18(2):731-8. doi: 10.52711/0974-360X.2025.00108
Cite(Electronic):
Khushi Shah, Kanika Nadar, Prashasti Kanikar. Automated Classification of Cells from Bone Marrow Cytology with Deep Learning. Research Journal of Pharmacy and Technology.2025;18(2):731-8. doi: 10.52711/0974-360X.2025.00108 Available on: https://rjptonline.org/AbstractView.aspx?PID=2025-18-2-40
REFERENCES:
1. Swerdlow SH, et al. The 2016 revision of the World Health Organization classification of lymphoid neoplasms. Blood. 2016; 127(20): 2375-2390. doi:10.1182/blood-2016-01-643569
2. Döhner H, et al. Diagnosis and management of AML in adults: 2017 ELN recommendations from an international expert panel. Blood. 2017; 129(4): 424-447. doi:10.1182/blood-2016-08-733196
3. Briggs C, et al. Can automated blood film analysis replace the manual differential? An evaluation of the CellaVision DM96 automated image analysis system. Int J Lab Hematol. 2009; 31(1): 48-60. doi:10.1111/j.1751-553X.2007.01002.x
4. Angulo J, Flandrin G. Automated detection of working area of peripheral blood smears using mathematical morphology. Anal Cell Pathol. 2003; 25(1): 37-49. doi:10.1155/2003/642562
5. A. S. Abeed, et al. BoMaCNet: A Convolutional Neural Network Model to Detect Bone Marrow Cell Cytology. 2022 25th International Conference on Computer and Information Technology (ICCIT), Cox's Bazar, Bangladesh. 2022; 686-691. doi: 10.1109/ICCIT57492.2022.10054976
6. Tayebi RM, et al. Automated bone marrow cytology using deep learning to generate a histogram of cell types. Communications Medicine. 2022; 2(1). doi:https://doi.org/10.1038/s43856-022-00107-6
7. Chitra P, et al. Detection of AML in Blood Microscopic Images using Local Binary Pattern and Supervised Classifier. Research Journal of Pharmacy and Technology. 2019; 12(4): 1717-1720. doi: 10.5958/0974-360X.2019.00286.5
8. Tripathi S, et al. HematoNet: Expert level classification of bone marrow cytology morphology in hematological malignancy with deep learning. Artificial Intelligence in the Life Sciences. 2022; 2: 100043. doi:https://doi.org/10.1016/j.ailsci.2022.100043
9. Krappe S, et al. Automated classification of bone marrow cells in microscopic images for diagnosis of leukemia: a comparison of two classification schemes with respect to the segmentation quality. Proceedings of SPIE. 2015. doi:https://doi.org/10.1117/12.2081946
10. Reta C, et al. Correction: Segmentation and Classification of Bone Marrow Cells Images Using Contextual Information for Medical Diagnosis of Acute Leukemias. PLOS ONE. 2015; 10(7): e0134066-e0134066. doi:https://doi.org/10.1371/journal.pone.0134066
11. Chandradevan R, et al. Machine-based detection and classification for bone marrow aspirate differential counts: initial development focusing on nonneoplastic cells. Lab Invest. 2020; 100(1): 98-109. doi:10.1038/s41374-019-0325-7
12. Song TH, et al. Simultaneous Cell Detection and Classification in Bone Marrow Histology Images. IEEE J Biomed Health Inform. 2019; 23(4): 1469-1476. doi:10.1109/JBHI.2018.2878945
13. Krappe S, et al. Automated morphological analysis of bone marrow cells in microscopic images for diagnosis of leukemia: nucleus-plasma separation and cell classification using a hierarchical tree model of hematopoesis. Proceedings of SPIE. 2016. doi:https://doi.org/10.1117/12.2216037
14. Scotti F. Automatic morphological analysis for acute leukemia identification in peripheral blood microscope images. IEEE Xplore. 2005. doi:https://doi.org/10.1109/CIMSA.2005.1522835
15. Sindhu J, Renee N. Impact of Artificial Intelligence in chosen Indian Commercial Bank –A Cost Benefit Analysis. Asian Journal of Management. 2019; 10(4): 377-384.
16. Aayush K, et al. Application of Artificial Intelligence in Curbing Air Pollution: The Case of India. Asian Journal of Management. 2020; 11(3): 285-290.
17. Patel A, et al. Explicating Artificial Intelligence: Applications in Medicine and Pharmacy. Asian Journal of Pharmacy and Technology; 12(4): 401-6. doi: 10.52711/2231-5713.2022.00061
18. Sigatapu L, et al. Artificial Intelligence in Healthcare- An Overview. Asian Journal of Pharmacy and Technology. 2023; 13(3): 218-2. doi: 10.52711/2231-5713.2023.00039
19. Mori J, et al. Assessment of dysplasia in bone marrow smear with convolutional neural network. Scientific Reports. 2020; 10(1). doi:https://doi.org/10.1038/s41598-020-71752-x
20. Anilkumar KK, et al. A survey on image segmentation of blood and bone marrow smear images with emphasis to automated detection of Leukemia. Biocybernetics and Biomedical Engineering. 2020; 40(4): 1406-1420. doi:https://doi.org/10.1016/j.bbe.2020.08.010
21. Jin H, et al. Developing and Preliminary Validating an Automatic Cell Classification System for Bone Marrow Smears: a Pilot Study. J Med Syst. 2020; 44(10): 184. doi:10.1007/s10916-020-01654-y
22. Choi JW, et al. White blood cell differential count of maturation stages in bone marrow smear using dual-stage convolutional neural networks. PLOS ONE. 2017; 12(12): e0189259. doi:https://doi.org/10.1371/journal.pone.0189259
23. Greenspan H, et al. Guest Editorial Deep Learning in Medical Imaging: Overview and Future Promise of an Exciting New Technique. IEEE Transactions on Medical Imaging. 2016; 35(5): 1153-1159. doi:https://doi.org/10.1109/tmi.2016.2553401
24. Shen D, et al. Deep Learning in Medical Image Analysis. Annu Rev Biomed Eng. 2017; 19: 221-248. doi:10.1146/annurev-bioeng-071516-044442
25. Matek C, et al. Highly accurate differentiation of bone marrow cell morphologies using deep neural networks on a large image data set. Blood. 2021; 138(20): 1917-1927. doi:https://doi.org/10.1182/blood.2020010568
26. Matek C, et al. An Expert-Annotated Dataset of Bone Marrow Cytology in Hematologic Malignancies [Data set]. The Cancer Imaging Archive. 2021. doi: https://doi.org/10.7937/TCIA.AXH3-T579
27. Clark K, et al. The Cancer Imaging Archive (TCIA): maintaining and operating a public information repository. J Digit Imaging. 2013; 26(6): 1045-1057. doi:10.1007/s10278-013-9622-7
28. Swaroop S. A Naive and Semantic Approach for Detecting Face Mask Region Based Convolutional Neural Networks (R-CNN). Research Journal of Engineering and Technology. 2021; 12(4): 105-9. doi: 10.52711/2321-581X.2021.00018
29. Rajarajeswari. S, et al. Skin Cancer Detection using Deep Learning. Research Journal of Pharmacy and Technology. 2022; 15(10): 4519-5. doi: 10.52711/0974-360X.2022.00758
30. Rajasekar B. Brain Tumour Segmentation using CNN and WT. Research Journal of Pharmacy and Technology. 2019; 12(10): 4613-4617. doi: 10.5958/0974-360X.2019.00793.5
31. Karthiga M, et al. Malevolent Melanoma diagnosis using Deep Convolution Neural Network. Research Journal of Pharmacy and Technology. 2020; 13(3): 1248-1252. doi: 10.5958/0974-360X.2020.00230.9
32. Liu Z, et al. A ConvNet for the 2020s. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 2022. doi:https://doi.org/10.1109/cvpr52688.2022.01167
33. Huang G, et al. Densely Connected Convolutional Networks. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2017. doi:https://doi.org/10.1109/cvpr.2017.243
34. Tan M, Le Q. EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks. Proceedings of the 36th International Conference on Machine Learning. 2019. available on http://proceedings.mlr.press/v97/tan19a.html
35. He K, Zhang X, Ren S, Sun J. Deep Residual Learning for Image Recognition. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2016. doi:https://doi.org/10.1109/cvpr.2016.90
36. Simonyan K, Zisserman A. Very Deep Convolutional Networks for Large-Scale Image Recognition. International Conference on Learning Representations. 2015. doi: https://doi.org/10.48550/arXiv.1409.1556
37. Chollet F. Xception: Deep Learning with Depthwise Separable Convolutions. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2017. doi:https://doi.org/10.1109/cvpr.2017.195
38. Patel S, Shah S. Artificial Intelligence: Comprehensive Overview and its Pharma Application. Asian Journal of Pharmacy and Technology. 12(4): 337-8. doi: 10.52711/2231-5713.2022.00054