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

Nayeon Kim

doi:doi:10.11648/j.ajbls.20210905.19

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

Nayeon Kim

Published in American Journal of Biomedical and Life Sciences (Volume 9, Issue 5)

Received: 8 October 2021 Accepted: 27 October 2021 Published: 30 October 2021

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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

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[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.
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[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.
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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

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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

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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

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@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}
}

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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  -

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Author Information

Nayeon Kim

The Suwon University Lab, Suwon, Korea

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Plain Text BibTeX RIS

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

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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

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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

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@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  -

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