Exploring Convolutional Neural Networks for the Classification of Acute Lymphoblast Leukemia Blood Cell Images
Andrey Trubnikov
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Samara National Research University, Russia
Paper #9045 received 15 Dec 2023; revised manuscript received 20 Dec 2023; accepted for publication 26 Dec 2023; published online 20 Feb 2024.
DOI: 10.18287/JBPE24.10.010302
Abstract
This paper introduces a novel approach to blood cell classification using convolutional neural networks (CNNs). Our emphasis lies in methodological insights derived from comparative analyses across various CNN architectures, implemented in Python with PyTorch such as ResNet152V2, Xception, EfficientNetB5, and EfficientNetV2M. In exploring model capabilities and limitations, we observe that the simple and shallow architectures is not sufficient to learn patterns compared to more complex networks. During research we have discovered that EfficientNetV2M demonstrate stable results with mean F1=0.891 score, which is higher compare to other models. Saliency maps are applied to reveal significant regions within or near cells, offering nuanced insights into morphological influences like cell shape. Extensive data augmentation allows one effectively mitigate overfitting, aligning train learning curves with validation and test splits. Our study is methodologically rich, comprising a meticulous data-specific overview, a robustness analysis through multiple model training, a systematic architecture comparison, and an in-depth examination using saliency maps.
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1. I. Milevsky, Acute leukemias – history, causes, MedUniver, 18 March 2021 (accessed 23 May 2022). [https://meduniver.com/Medical/gematologia/ostrie_leikozi.html].
2. Overview Acute lymphoblastic leukaemia, UK (accessed 15 September 2022). [https://www.nhs.uk/conditions/acute-lymphoblastic-leukaemia].
3. W. Ladines-Castro, G. Barragan-Ibanez, M. Luna-Perez, A. Santoyo-Sánchez, J. Collazo-Jaloma, E. Mendoza-García, and C.O. Ramos-Peñafiel, “Morphology of leukaemias,” Revista Médica del Hospital General de México 79(2), 107–113 (2018).
4. Y. Gu, A. Chen, X. Zhang, C. Fan, K. Li, and J. Shen, “Deep Learning based Cell Classification in Imaging FlowCytometer,” ASP Transactions on Pattern Recognition and Intelligent Systems 1(2), 18–27 (2021).
5. S. Shafique, S. Tehsin, “Acute lymphoblastic leukemia detection and classification of its subtypes using pretrained deep convolutional neural networks,” Technology in Cancer Research & Treatment 17, 153303381880278 (2018).
6. T. Pansombut, S. Wikaisuksakul, K. Khongkraphan, and A. Phon-On “Convolutional Neural Networks for Recognition of Lymphoblast Cell Images,” Computational Intelligence and Neuroscience 2019, 7519603 (2019).
7. R. Cuocolo, M. Caruso, T. Perillo, L. Ugga, and M. Petretta, “Machine learning in oncology: a clinical appraisal,” Cancer Letters 481, 55–62 (2020).
8. M. Khushi, K. Shaukat, T. M. Alam, I. A. Hameed, S. Uddin, S. Luo, X. Yang, and M. C. Reyes, “A comparative performance analysis of data resampling methods on imbalance medical data,” IEEE Access 9, 109960–109975 (2021).
9. C. Lu, M. Chen, J. Shen, L. L. Wang, and C. C. Hsu “Removal of salt-and-pepper noise for X-ray bio-images using pixel-variation gain factors,” Computers & Electrical Engineering 71, 862–876 (2018).
10. N. J. Everall, “Confocal Raman microscopy: common errors and artefacts,” Analyst 135(10), 2512–2522 (2010).
11. J. Ludzik, C. Lee, A. Witkowski, and J. Hetts, “Minimizing sampling error using a unique ink–stained reflectance confocal microscopy-guided biopsy technique to diagnose a large lentigo maligna,” JAAD Case Reports 24, 118–120 (2022).
12. W. Yu, W. K. Dodds, M. K. Banks, J. Skalsky, and E. A. Strauss, “Optimal staining and sample storage time for direct microscopic enumeration of total and active bacteria in soil with two fluorescent dyes,” Applied and Environmental Microbiology 61(9), 3367–3372 (1995).
13. R. Laine, G. Jacquemet, and A. Krull, “Imaging in focus: An introduction to denoising bioimages in the era of deep learning,” The International Journal of Biochemistry & Cell Biology 140, 106077 (2021).
14. T. Parag, A. Chakraborty, S. Plaza, and L. Scheffer, “A context-aware delayed agglomeration framework for electron microscopy segmentation,” PloS One 10(5), e0125825 (2015).
15. S. U. Akram, J. Kannala, L. Eklund, and J. Heikkilä, “Cell segmentation proposal network for microscopy image analysis,” in Deep Learning and Data Labeling for Medical Applications, G. Carneiro, D. Mateus, L. Peter, A. Bradley, J. M. R. S. Tavares, V. Belagiannis, J. P. Papa, J. C. Nascimento, M. Loog, Z. Lu, J. S. Cardoso, and J. Cornebise (Eds.), Springer International Publishing, 10008, 21–29 (2016).
16. A. Venkataramanan, M. Laviale, C. Figus, P. Usseglio–Polatera, and C. Pradalier, “Tackling inter-class similarity and intra–class variance for microscopic image-based classification,” in Computer Vision Systems, M. Vincze, T. Patten, H. I. Christensen, L. Nalpantidis, and M. Liu (Eds.), Springer International Publishing, 12899, 93–103. (2021).
17. H. Rizk, A. Shokry, and M. Youssef, “Effectiveness of data augmentation in cellular-based localization using deep learning,” in IEEE Wireless Communications and Networking Conference, 1–6 (2019).
18. S. Yu, S. Zhang, B. Wang, H. Dun, L. Xu, X. Huang, and X. Feng, “Generative adversarial network based data augmentation to improve cervical cell classification model,” Mathematical Biosciences and Engineering 18, 1740–1752 (2021).
19. M. Momeny, A. M. Latif, M. A. Sarram, R. Sheikhpour, and Y. D. Zhang “A noise robust convolutional neural network for image classification,” Results in Engineering 10, 100225 (2021).
20. “Leukemia Classification,” US (accessed 16 Febrary 2024) [https://www.kaggle.com/datasets/andrewmvd/leukemia-classification].
21. M. Tan, Q. V. Le, “EfficientNet: rethinking model scaling for convolutional neural networks,” International Conference on Machine Learning 10, 6105–6114 (2019).
22. G. Kyriakides, K. Margaritis, “An introduction to neural architecture search for convolutional networks,” arXiv preprint arXiv:2005.11074v1 (2020).
23. X. Yin, W. Chen, X. Wu, and H Yue, “Fine-tuning and visualization of convolutional neural networks,” 12th IEEE Conference on Industrial Electronics and Applications (ICIEA), 1310–1315 (2017).
24. G. Chelnlei, L. Zhang, “A Novel Multiresolution Spatiotemporal Saliency Detection Model and Its Applications in Image and Video Compression,” IEEE Transactions on Image Processing 19(1), 185–198 (2010)
25. R. Monroy, S. Lutz, T. Chalasani, and V. Smolic, “Salnet360: Saliency maps for omni-directional images with CNN,” Signal Processing: Image Communication 69, 26–34 (2018).
26. K. Nagasubramanian, S. Jones, A. K. Singh, B. Ganapathysubramanian, and S. Sarkar “Explaining hyperspectral imaging based plant disease identification: 3D CNN and saliency maps,” arXiv preprint arXiv:1804.08831 (2018).
27. A. Alqaraawi, M. Schuessler, P. Weiß, E. Costanza, and N. Berthouze, “Evaluating saliency map explanations for convolutional neural networks: a user study,” Proceedings of the 25th International Conference on Intelligent User Interfaces, 275–285 (2020).
28. A. Rehman, N. Abbas, T. Saba, S. I. Rahman, Z. Mehmood, and H. Kolivand, “Classification of acute lymphoblastic leukemia using deep learning,” Microscopy Research and Technique 81(11), 1310–1317 (2018).
29. A. Genovese, M. S. Hosseini, V. Piuri, K. N. Plataniotis, and F. Scotti, “Acute lymphoblastic leukemia detection based on adaptive unsharpening and deep learning,” in ICASSP 2021–2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 1205–1209 (2021).
30. P. Chlap, H. Min, N. Vandenberg, J. Dowling, L. Holloway, and A. Haworth, “A review of medical image data augmentation techniques for deep learning applications,” Journal of Medical Imaging and Radiation Oncology 65(5), 545–563 (2021).
31. F. Garcea, A. Serra, F. Lamberti, and L. Morra, “Data augmentation for medical imaging: A systematic literature review,” Computers in Biology and Medicine 152, 106391 (2023).
32. L. H. S. Vogado, R. M. S. Veras, F. H. D. Araujo, R. R.V. Silva, and K. R.T. Aires, “Leukemia diagnosis in blood slides using transfer learning in CNNs and SVM for classification,” Engineering Applications of Artificial Intelligence 72, 415–422 (2018).
33. D. Picard, “Torch. manual_seed (3407) is all you need: On the influence of random seeds in deep learning architectures for computer vision,” arXiv preprint arXiv:2109.08203 (2021).
34. P. Madhyastha, R. Jain, “On model stability as a function of random seed,” arXiv preprint arXiv:1909.10447 (2019).
35. J. Luengo, S. García, and F. Herrera, “A study on the use of statistical tests for experimentation with neural networks: Analysis of parametric test conditions and non-parametric tests,” Expert Systems with Applications 36(4), 7798–7808 (2009).
36. N. Arun, N. Gaw, P. Singh, K. Chang, M. Aggarwal, B. Chen, K. Hoebel, S. Gupta, J. Patel, M. Gidwani, J. Adebayo, M. D. Li, and J. Kalpathy-Cramer, “Assessing the trustworthiness of saliency maps for localizing abnormalities in medical imaging,” Radiology: Artificial Intelligence 3(6), e200267 (2021).
37. N. T. Arun, N. Gaw, P. Singh, K. Chang, K. V. Hoebel, J. Patel, M. Gidwani, and J. Kalpathy-Cramer, “Assessing the validity of saliency maps for abnormality localization in medical imaging,” arXiv preprint arXiv:2006.00063 (2020).
38. M. Aggarwal, N. Arun, S. Gupta, A. Vaswani, B. Chen, M. Li, K. Chang, J. Patel, K. Hoebel, M. Gidwani, J. Kalpathy-Cramer, and P. Singh, “Towards trainable saliency maps in medical imaging,” arXiv preprint arXiv:2011.07482 (2020).
39. C. Andreou, D. Rogge, and R. Müller, “A New Approach for Endmember Extraction and Clustering Addressing Inter– and Intra–Class Variability via Multiscaled–Band Partitioning,” in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 9(9), 4215–4231 (2016).
40. D. Lee, K. Kim, “Improved Noise–filtering Algorithm for AdaBoost Using the Inter–and Intra–class Variability of Imbalanced Datasets,” Journal of Intelligent & Fuzzy Systems 43(4), 5035–5051 (2022).
41. Y. Liu, Y. Zhang, Y. Wang, F. Hou, J. Yuan, J. Tian, Y. Zhang, Z. Shi, J. Fan, and Z. He, “A survey of visual transformers,” IEEE Transactions on Neural Networks and Learning Systems (2023).
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