An Attention-Based Convolutional Neural Network for Acute Lymphoblastic Leukemia Classification
Source abstract
Abstract
Leukemia is a kind of blood cancer that influences people of all ages and is one of the leading causes of death worldwide. Acute lymphoblastic leukemia (ALL) is the most widely recognized type of leukemia found in the bone marrow of the human body. Traditional disease diagnostic techniques like blood and bone marrow examinations are slow and painful, resulting in the demand for non-invasive and fast methods. This work presents a non-invasive, convolutional neural network (CNN) based approach that utilizes medical images to perform the diagnosis task. The proposed solution consisting of a CNN-based model uses an attention module called Efficient Channel Attention (ECA) with the visual geometry group from oxford (VGG16) to extract better quality deep features from the image dataset, leading to better feature representation and better classification results. The proposed method shows that the ECA module helps to overcome morphological similarities between ALL cancer and healthy cell images. Various augmentation techniques are also employed to increase the quality and quantity of training data. We used the classification of normal vs. malignant cells (C-NMC) dataset and divided it into seven folds based on subject-level variability, which is usually ignored in previous methods. Experimental results show that our proposed CNN model can successfully extract deep features and achieved an accuracy of 91.1%. The obtained findings show that the proposed method may be utilized to diagnose ALL and would help pathologists.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Digital Imaging for Blood Diseases · AI in cancer detection · COVID-19 diagnosis using AI
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Information Science and Engineering, Shandong Normal University, Jinan 250358, China
Director of Shandong Provincial Key Laboratory of Higher Education, Dean of School of Information Science and Engineering, Vice President of Life and Health Research Institute, Shandong Normal University, Jinan 250358, China
Funding
Natural Science Foundation of Shandong Province · Taishan Scholar Project of Shandong Province · National Natural Science Foundation of China
References (60)
Show source-provided references
1. Classification of acute leukemia using medical-knowledge-based morphology and CD marker10.1016/j.bspc.2018.01.020 ↗
2. Leukemia diagnosis in blood slides using transfer learning in CNNs and SVM for classification10.1016/j.engappai.2018.04.024 ↗
3. American Society of Hematology (2021, April 24). Hematology. Available online: https://www.hematology.org.
4. Key Statistics for Acute Lymphocytic Leukemia (2021, April 24). American Cancer Society. Available online: https://www.cancer.org/cancer/acute-lymphocytic-leukemia/about/key-statistics.html.
5. Curesearch for Childrens Cancer Research (2021, April 20). Curesearch. Available online: https://curesearch.org/Acute-Lymphoblastic-Leukemia-in-Children.
6. Mohamed, M., Far, B., and Guaily, A. (2012, January 14–17). An efficient technique for white blood cells nuclei automatic segmentation. Proceedings of the 2012 IEEE International Conference on Systems, Man, and Cybernetics (SMC), Seoul, Korea.10.1109/ICSMC.2012.6377703 ↗
7. Recognition of acute lymphoblastic leukemia cells in microscopic images using k-means clustering and support vector machine classifier10.4103/2228-7477.150428 ↗
8. Duggal, R., Gupta, A., and Gupta, R. (2016). Segmentation of overlapping/touching white blood cell nuclei using artificial neural networks. CME Series on Hemato-Oncopathology, All India Institute of Medical Sciences (AIIMS).
9. Duggal, R., Gupta, A., Gupta, R., and Mallick, P. (2017, January 10–14). SD-layer: Stain deconvolutional layer for CNNs in medical microscopic imaging. Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention, Quebec City, QC, Canada.10.1007/978-3-319-66179-7_50 ↗
10. Duggal, R., Gupta, A., Gupta, R., Wadhwa, M., and Ahuja, C. (2016, January 18–22). Overlapping cell nuclei segmentation in microscopic images using deep belief networks. Proceedings of the Tenth Indian Conference on Computer Vision, Graphics and Image Processing, Guwahati, India.10.1145/3009977.3010043 ↗
11. GCTI-SN: Geometry-inspired chemical and tissue invariant stain normalization of microscopic medical images10.1016/j.media.2020.101788 ↗
12. Stain color normalization and segmentation of plasma cells in microscopic images as a prelude to development of computer assisted automated disease diagnostic tool in multiple myeloma
13. Bayramoglu, N., and Heikkilä, J. (2016, January 11–14). Transfer learning for cell nuclei classification in histopathology images. Proceedings of the European Conference on Computer Vision, Amsterdam, The Netherlands.10.1007/978-3-319-49409-8_46 ↗
14. HEp-2 cell image classification with deep convolutional neural networks10.1109/JBHI.2016.2526603 ↗
15. Automatic detection and classification of leukocytes using convolutional neural networks10.1007/s11517-016-1590-x ↗
16. White blood cells segmentation and classification to detect acute leukemia
17. An ensemble classifier system for early diagnosis of acute lymphoblastic leukemia in blood microscopic images10.1007/s00521-013-1438-3 ↗
18. Leucocyte classification for leukaemia detection using image processing techniques10.1016/j.artmed.2014.09.002 ↗
19. Singhal, V., and Singh, P. (March, January 28). Local binary pattern for automatic detection of acute lymphoblastic leukemia. Proceedings of the 2014 Twentieth National Conference on Communications (NCC), Kanpur, India.10.1109/NCC.2014.6811261 ↗
20. Automated leukaemia detection using microscopic images10.1016/j.procs.2015.08.082 ↗
21. Microscopic image segmentation using fuzzy c means for leukemia diagnosis
22. Mohamed, H., Omar, R., Saeed, N., Essam, A., Ayman, N., Mohiy, T., and AbdelRaouf, A. (2018, January 29). Automated detection of white blood cells cancer diseases. Proceedings of the 2018 First International Workshop on Deep and Representation Learning (IWDRL), Cairo, Egypt.10.1109/IWDRL.2018.8358214 ↗
23. Rajpurkar, P., Irvin, J., Zhu, K., Yang, B., Mehta, H., Duan, T., Ding, D., Bagul, A., Langlotz, C., and Shpanskaya, K. (2017). Chexnet: Radiologist-level pneumonia detection on chest x-rays with deep learning. arXiv.
24. Yan, Z., Zhan, Y., Peng, Z., Liao, S., Shinagawa, Y., Metaxas, D.N., and Zhou, X.S. (July, January 28). Bodypart recognition using multi-stage deep learning. Proceedings of the International Conference on Information Processing in Medical Imaging, Sabhal Mor Ostaig/Isle of Skye, UK.
25. Sermanet, P., Eigen, D., Zhang, X., Mathieu, M., Fergus, R., and LeCun, Y. (2013). Overfeat: Integrated recognition, localization and detection using convolutional networks. arXiv.
26. Quicksilver: Fast predictive image registration–a deep learning approach10.1016/j.neuroimage.2017.07.008 ↗
27. Stefano, A., and Comelli, A. (2021). Customized Efficient Neural Network for COVID-19 Infected Region Identification in CT Images. J. Imaging, 7.10.3390/jimaging7080131 ↗
28. Comelli, A., Dahiya, N., Stefano, A., Vernuccio, F., Portoghese, M., Cutaia, G., Bruno, A., Salvaggio, G., and Yezzi, A. (2021). Deep learning-based methods for prostate segmentation in magnetic resonance imaging. Appl. Sci., 11.10.3390/app11020782 ↗
29. Classification of acute lymphoblastic leukemia using deep learning10.1002/jemt.23139 ↗
30. Acute lymphoblastic leukemia detection and classification of its subtypes using pretrained deep convolutional neural networks10.1177/1533033818802789 ↗
31. Habibzadeh, M., Jannesari, M., Rezaei, Z., Baharvand, H., and Totonchi, M. (2017, January 13–15). Automatic white blood cell classification using pre-trained deep learning models: Resnet and inception. Proceedings of the Tenth International Conference on Machine Vision (ICMV 2017), International Society for Optics and Photonics, Vienna, Austria.10.1117/12.2311282 ↗
32. Ahmed, N., Yigit, A., Isik, Z., and Alpkocak, A. (2019). Identification of leukemia subtypes from microscopic images using convolutional neural network. Diagnostics, 9.10.3390/diagnostics9030104 ↗
33. Spectral-spatial feature-based neural network method for acute lymphoblastic leukemia cell identification via microscopic hyperspectral imaging technology10.1364/BOE.8.003017 ↗
34. Pansombut, T., Wikaisuksakul, S., Khongkraphan, K., and Phon-On, A. (2019). Convolutional neural networks for recognition of lymphoblast cell images. Comput. Intell. Neurosci., 2019.10.1155/2019/7519603 ↗
35. SDCT-AuxNetθ: DCT augmented stain deconvolutional CNN with auxiliary classifier for cancer diagnosis10.1016/j.media.2020.101661 ↗
36. Kasani, P.H., Park, S.W., and Jang, J.W. (2020). An Aggregated-Based Deep Learning Method for Leukemic B-lymphoblast Classification. Diagnostics, 10.10.3390/diagnostics10121064 ↗
37. Kassani, S.H., Kassani, P.H., Wesolowski, M.J., Schneider, K.A., and Deters, R. (2019, January 16–18). A hybrid deep learning architecture for leukemic B-lymphoblast classification. Proceedings of the 2019 International Conference on Information and Communication Technology Convergence (ICTC), Jeju Island, Korea.10.1109/ICTC46691.2019.8939959 ↗
38. Kant, S., Kumar, P., Gupta, A., and Gupta, R. (2018). Leukonet: Dct-based cnn architecture for the classification of normal versus leukemic blasts in b-all cancer. arXiv.
39. Labati, R.D., Piuri, V., and Scotti, F. (2011, January 11–14). All-IDB: The acute lymphoblastic leukemia image database for image processing. Proceedings of the 2011 18th IEEE International Conference on Image Processing, Brussels, Belgium.10.1109/ICIP.2011.6115881 ↗
40. Brain tumor segmentation in multi-spectral MRI using convolutional neural networks (CNN)10.1002/jemt.22994 ↗
41. Cireşan, D.C., Meier, U., Masci, J., Gambardella, L.M., and Schmidhuber, J. (2011). High-performance neural networks for visual object classification. arXiv.
42. Imagenet classification with deep convolutional neural networks
43. Wong, S.C., Gatt, A., Stamatescu, V., and McDonnell, M.D. (December, January 30). Understanding data augmentation for classification: When to warp?. Proceedings of the 2016 International Conference on Digital Image Computing: Techniques and Applications (DICTA), Gold Coast, Australia.10.1109/DICTA.2016.7797091 ↗
44. Perez, L., and Wang, J. (2017). The effectiveness of data augmentation in image classification using deep learning. arXiv.
45. Shijie, J., Ping, W., Peiyi, J., and Siping, H. (2017, January 20–22). Research on data augmentation for image classification based on convolution neural networks. Proceedings of the 2017 Chinese Automation Congress (CAC), Jinan, China.10.1109/CAC.2017.8243510 ↗
46. Mikołajczyk, A., and Grochowski, M. (2018, January 9–12). Data augmentation for improving deep learning in image classification problem. Proceedings of the 2018 International Interdisciplinary PhD Workshop (IIPhDW), Swinoujscie, Poland.10.1109/IIPHDW.2018.8388338 ↗
47. Huang, G., Liu, Z., Van Der Maaten, L., and Weinberger, K.Q. (2017, January 21–26). Densely connected convolutional networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.10.1109/CVPR.2017.243 ↗
48. He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 26–27). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.10.1109/CVPR.2016.90 ↗
49. Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A. (2015, January 7–12). Going deeper with convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.10.1109/CVPR.2015.7298594 ↗
50. Xiao, F., Kuang, R., Ou, Z., and Xiong, B. (2019, January 8–11). DeepMEN: Multi-model Ensemble Network for B-Lymphoblast Cell Classification. Proceedings of the ISBI 2019 C-NMC Challenge: Classification in Cancer Cell Imaging, Venicee, Italy.10.1007/978-981-15-0798-4_9 ↗
51. Simonyan, K., and Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv.
52. Qilong, W., Banggu, W., Pengfei, Z., Peihua, L., Wangmeng, Z., and Qinghua, H. (2020, January 16–18). ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, (CVPR), Seattle, WA, USA.
53. Xu, K., Ba, J., Kiros, R., Cho, K., Courville, A., Salakhudinov, R., Zemel, R., and Bengio, Y. (2015, January 7–9). Show, attend and tell: Neural image caption generation with visual attention. Proceedings of the International Conference on Machine Learning, PMLR, Lille, France.
54. Pan, Y., Liu, M., Xia, Y., and Shen, D. (2019, January 8–11). Neighborhood-correction algorithm for classification of normal and malignant cells. Proceedings of the ISBI 2019 C-NMC Challenge: Classification in Cancer Cell Imaging, Venicee, Italy.10.1007/978-981-15-0798-4_8 ↗
55. Verma, E., and Singh, V. (2019, January 8–11). ISBI Challenge 2019: Convolution Neural Networks for B-ALL Cell Classification. Proceedings of the ISBI 2019 C-NMC Challenge: Classification in Cancer Cell Imaging, Venicee, Italy.10.1007/978-981-15-0798-4_14 ↗
56. Shi, T., Wu, L., Zhong, C., Wang, R., and Zheng, W. (2019, January 8–11). Ensemble Convolutional Neural Networks for Cell Classification in Microscopic Images. Proceedings of the ISBI 2019 C-NMC Challenge: Classification in Cancer Cell Imaging, Venicee, Italy.10.1007/978-981-15-0798-4_5 ↗
57. Liu, Y., and Long, F. (2019, January 8–11). Acute lymphoblastic leukemia cells image analysis with deep bagging ensemble learning. Proceedings of the ISBI 2019 C-NMC Challenge: Classification in Cancer Cell Imaging, Venicee, Italy.10.1101/580852 ↗
58. Shah, S., Nawaz, W., Jalil, B., and Khan, H.A. (2019, January 8–11). Classification of normal and leukemic blast cells in B-ALL cancer using a combination of convolutional and recurrent neural networks. Proceedings of the ISBI 2019 C-NMC Challenge: Classification in Cancer Cell Imaging, Venicee, Italy.10.1007/978-981-15-0798-4_3 ↗
59. Ding, Y., Yang, Y., and Cui, Y. (2019, January 8–11). Deep learning for classifying of white blood cancer. Proceedings of the ISBI 2019 C-NMC Challenge: Classification in Cancer Cell Imaging, Venicee, Italy.10.1007/978-981-15-0798-4_4 ↗
60. Xie, X., Li, Y., Zhang, M., Wu, Y., and Shen, L. (2019, January 8–11). Multi-streams and Multi-features for Cell Classification. Proceedings of the ISBI 2019 C-NMC Challenge: Classification in Cancer Cell Imaging, Venicee, Italy.10.1007/978-981-15-0798-4_10 ↗