| Peer-Reviewed

Deep Learning Technology-based Model to Identify Benign and Pro-B Acute Lymphoblastic Leukemia (ALL): Xception + LIME

Received: 8 October 2021     Accepted: 27 October 2021     Published: 30 October 2021
Views:       Downloads:
Abstract

Leukemia is a type of cancer that occurs when abnormal blood cells take place in the bone marrow. Leukemia can either be acute (fastly growing) or chronic (slowly growing) and it is considered as one of the most commonly diagnosed cancers for children younger than the age of 15 or adults older than the age of 55. Leukemia can be diagnosed through various types of tests and depending on the aggressiveness of the disease, the treatment may differ. To provide a low-cost, time-efficient solution, this study employs the deep learning technique to train the Xception, VGG16, VGG19, and MobileNet models to optimize the accuracy of medical image detection. Through medical imaging, the trained model is able to detect anomalies in the dataset and identify whether the given data is a benign acute lymphoblastic leukemia (ALL) or a Pro-B ALL. Overall, this VGG16 showed the most optimal performance in terms of accuracy and precision, producing a 98.5% accuracy in detecting abnormal regions from the dataset. This study also further used XAI technique and a deep convolutional neural network to visualize the results of anomalies. As a result, this paper concluded that both deep learning and machine learning techniques are yet to replace human resources and intelligence as the heatmap and the LIME portrayal identified different regions as abnormal parts, therefore proving the inconsistency of deep learning technology.

Published in American Journal of Biomedical and Life Sciences (Volume 9, Issue 5)
DOI 10.11648/j.ajbls.20210905.19
Page(s) 279-285
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2021. Published by Science Publishing Group

Keywords

Deep Learning, Machine Learning, Leukemia, XAI, Xception

References
[1] Watson, S. (2019, May 11). Symptoms of Leukemia in Pictures: Rashes and Bruises. Healthline. https://www.healthline.com/health/pictures-leukemia-rashes-bruises#symptoms.
[2] American Cancer Society: Cancer Facts & Statistics. (2021, October 8). Leukemia. https://cancerstatisticscenter.cancer.org/#!/cancer-site/Leukemia.
[3] Gao, C. (2019, November 14). Clinical-biological characteristics and treatment outcomes of pediatric pro-B ALL patients enrolled in BCH-2003 and CCLG-2008 protocol: a study of 121 Chinese children. Cancer Cell International. https://cancerci.biomedcentral.com/articles/10.1186/s12935-019-1013-9.
[4] Rehman, A., Abbas, N., Saba, T., Rahman, S. I. U., Mehmood, Z., & Kolivand, H. (2018). Classification of acute lymphoblastic leukemia using deep learning. Microscopy Research and Technique, 81 (11), 1310–1317. https://doi.org/10.1002/jemt.23139.
[5] Ramaneswaran, S., Srinivasan, K., Vincent, P. M. D. R., & Chang, C. Y. (2021). Hybrid Inception v3 XGBoost Model for Acute Lymphoblastic Leukemia Classification. Computational and Mathematical Methods in Medicine, 2021, 1–10. https://doi.org/10.1155/2021/2577375.
[6] 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, 1–14. https://doi.org/10.1155/2021/9933481.
[7] Loey, M., Naman, M., & Zayed, H. (2020). Deep Transfer Learning in Diagnosing Leukemia in Blood Cells. Computers, 9 (2), 29. https://doi.org/10.3390/computers9020029.
[8] Shaheen, M., Khan, R., Biswal, R. R., Ullah, M., Khan, A., Uddin, M. I., Zareei, M., & Waheed, A. (2021). Acute Myeloid Leukemia (AML) Detection Using AlexNet Model. Complexity, 2021, 1–8. https://doi.org/10.1155/2021/6658192.
[9] Acute Lymphoblastic Leukemia (ALL) image dataset. (2021, April 30). Kaggle. https://www.kaggle.com/mehradaria/leukemia.
[10] Sainath, T. N., Vinyals, O., Senior, A., & Sak, H. (2015). Convolutional, Long Short-Term Memory, fully connected Deep Neural Networks. 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). Published. https://doi.org/10.1109/icassp.2015.7178838.
[11] Albawi, S., Mohammed, T. A., & Al-Zawi, S. (2017). Understanding of a convolutional neural network. 2017 International Conference on Engineering and Technology (ICET). Published. https://doi.org/10.1109/icengtechnol.2017.8308186.
[12] Sahay, S., Omare, N., & Shukla, K. K. (2021). An Approach to identify Captioning Keywords in an Image using LIME. 2021 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS). Published. https://doi.org/10.1109/icccis51004.2021.9397159.
[13] Ribeiro, M. T., Singh, S., & Guestrin, C. (2016). “Why Should I Trust You?” Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Published. https://doi.org/10.1145/2939672.2939778.
[14] Chollet, F. (2017). Xception: Deep Learning with Depthwise Separable Convolutions. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Published. https://doi.org/10.1109/cvpr.2017.195.
[15] Yun, S., & Wong, A. (2021). Do All MobileNets Quantize Poorly? Gaining Insights into the Effect of Quantization on Depthwise Separable Convolutional Networks Through the Eyes of Multi-scale Distributional Dynamics. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 2447-2456).
[16] He, T., Guo, J., Chen, N., Xu, X., Wang, Z., Fu, K., Liu, L., & Yi, Z. (2020). MediMLP: Using Grad-CAM to Extract Crucial Variables for Lung Cancer Postoperative Complication Prediction. IEEE Journal of Biomedical and Health Informatics, 24 (6), 1762–1771. https://doi.org/10.1109/jbhi.2019.2949601.
Cite This Article
  • APA Style

    Nayeon Kim. (2021). Deep Learning Technology-based Model to Identify Benign and Pro-B Acute Lymphoblastic Leukemia (ALL): Xception + LIME. American Journal of Biomedical and Life Sciences, 9(5), 279-285. https://doi.org/10.11648/j.ajbls.20210905.19

    Copy | Download

    ACS Style

    Nayeon Kim. Deep Learning Technology-based Model to Identify Benign and Pro-B Acute Lymphoblastic Leukemia (ALL): Xception + LIME. Am. J. Biomed. Life Sci. 2021, 9(5), 279-285. doi: 10.11648/j.ajbls.20210905.19

    Copy | Download

    AMA Style

    Nayeon Kim. Deep Learning Technology-based Model to Identify Benign and Pro-B Acute Lymphoblastic Leukemia (ALL): Xception + LIME. Am J Biomed Life Sci. 2021;9(5):279-285. doi: 10.11648/j.ajbls.20210905.19

    Copy | Download

  • @article{10.11648/j.ajbls.20210905.19,
      author = {Nayeon Kim},
      title = {Deep Learning Technology-based Model to Identify Benign and Pro-B Acute Lymphoblastic Leukemia (ALL): Xception + LIME},
      journal = {American Journal of Biomedical and Life Sciences},
      volume = {9},
      number = {5},
      pages = {279-285},
      doi = {10.11648/j.ajbls.20210905.19},
      url = {https://doi.org/10.11648/j.ajbls.20210905.19},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajbls.20210905.19},
      abstract = {Leukemia is a type of cancer that occurs when abnormal blood cells take place in the bone marrow. Leukemia can either be acute (fastly growing) or chronic (slowly growing) and it is considered as one of the most commonly diagnosed cancers for children younger than the age of 15 or adults older than the age of 55. Leukemia can be diagnosed through various types of tests and depending on the aggressiveness of the disease, the treatment may differ. To provide a low-cost, time-efficient solution, this study employs the deep learning technique to train the Xception, VGG16, VGG19, and MobileNet models to optimize the accuracy of medical image detection. Through medical imaging, the trained model is able to detect anomalies in the dataset and identify whether the given data is a benign acute lymphoblastic leukemia (ALL) or a Pro-B ALL. Overall, this VGG16 showed the most optimal performance in terms of accuracy and precision, producing a 98.5% accuracy in detecting abnormal regions from the dataset. This study also further used XAI technique and a deep convolutional neural network to visualize the results of anomalies. As a result, this paper concluded that both deep learning and machine learning techniques are yet to replace human resources and intelligence as the heatmap and the LIME portrayal identified different regions as abnormal parts, therefore proving the inconsistency of deep learning technology.},
     year = {2021}
    }
    

    Copy | Download

  • TY  - JOUR
    T1  - Deep Learning Technology-based Model to Identify Benign and Pro-B Acute Lymphoblastic Leukemia (ALL): Xception + LIME
    AU  - Nayeon Kim
    Y1  - 2021/10/30
    PY  - 2021
    N1  - https://doi.org/10.11648/j.ajbls.20210905.19
    DO  - 10.11648/j.ajbls.20210905.19
    T2  - American Journal of Biomedical and Life Sciences
    JF  - American Journal of Biomedical and Life Sciences
    JO  - American Journal of Biomedical and Life Sciences
    SP  - 279
    EP  - 285
    PB  - Science Publishing Group
    SN  - 2330-880X
    UR  - https://doi.org/10.11648/j.ajbls.20210905.19
    AB  - Leukemia is a type of cancer that occurs when abnormal blood cells take place in the bone marrow. Leukemia can either be acute (fastly growing) or chronic (slowly growing) and it is considered as one of the most commonly diagnosed cancers for children younger than the age of 15 or adults older than the age of 55. Leukemia can be diagnosed through various types of tests and depending on the aggressiveness of the disease, the treatment may differ. To provide a low-cost, time-efficient solution, this study employs the deep learning technique to train the Xception, VGG16, VGG19, and MobileNet models to optimize the accuracy of medical image detection. Through medical imaging, the trained model is able to detect anomalies in the dataset and identify whether the given data is a benign acute lymphoblastic leukemia (ALL) or a Pro-B ALL. Overall, this VGG16 showed the most optimal performance in terms of accuracy and precision, producing a 98.5% accuracy in detecting abnormal regions from the dataset. This study also further used XAI technique and a deep convolutional neural network to visualize the results of anomalies. As a result, this paper concluded that both deep learning and machine learning techniques are yet to replace human resources and intelligence as the heatmap and the LIME portrayal identified different regions as abnormal parts, therefore proving the inconsistency of deep learning technology.
    VL  - 9
    IS  - 5
    ER  - 

    Copy | Download

Author Information
  • The Suwon University Lab, Suwon, Korea

  • Sections