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. 2022 Sep 20;10(10):1812.
doi: 10.3390/healthcare10101812.

Customized Deep Learning Classifier for Detection of Acute Lymphoblastic Leukemia Using Blood Smear Images

Niranjana Sampathila  1 , Krishnaraj Chadaga  2 , Neelankit Goswami  1 , Rajagopala P Chadaga  3 , Mayur Pandya  2 , Srikanth Prabhu  2 , Muralidhar G Bairy  1 , Swathi S Katta  4 , Devadas Bhat  1 , Sudhakara P Upadya  5
Affiliations

Customized Deep Learning Classifier for Detection of Acute Lymphoblastic Leukemia Using Blood Smear Images

Niranjana Sampathila et al. Healthcare (Basel). .

Abstract

Acute lymphoblastic leukemia (ALL) is a rare type of blood cancer caused due to the overproduction of lymphocytes by the bone marrow in the human body. It is one of the common types of cancer in children, which has a fair chance of being cured. However, this may even occur in adults, and the chances of a cure are slim if diagnosed at a later stage. To aid in the early detection of this deadly disease, an intelligent method to screen the white blood cells is proposed in this study. The proposed intelligent deep learning algorithm uses the microscopic images of blood smears as the input data. This algorithm is implemented with a convolutional neural network (CNN) to predict the leukemic cells from the healthy blood cells. The custom ALLNET model was trained and tested using the microscopic images available as open-source data. The model training was carried out on Google Collaboratory using the Nvidia Tesla P-100 GPU method. Maximum accuracy of 95.54%, specificity of 95.81%, sensitivity of 95.91%, F1-score of 95.43%, and precision of 96% were obtained by this accurate classifier. The proposed technique may be used during the pre-screening to detect the leukemia cells during complete blood count (CBC) and peripheral blood tests.

Keywords: acute lymphoblastic leukemia (ALL); blood smear; convolutional neural networks; deep learning; white blood cells.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Leukemia screening system.
Figure 2
Figure 2
Original images.
Figure 3
Figure 3
HIS color space.
Figure 4
Figure 4
Saturation component.
Figure 5
Figure 5
Images after thresholding.
Figure 6
Figure 6
Image after segmentation.
Figure 7
Figure 7
Images in the dataset: (a) represent blast cells; (b) represent healthy cell.
Figure 8
Figure 8
Original image of a blast cell and its augmented versions for the C_NMC_2019 dataset.
Figure 9
Figure 9
The architecture of CNN (ALLNet).
Figure 10
Figure 10
Prediction of classes of the test set images.
Figure 11
Figure 11
Accuracy for the five instances.
Figure 12
Figure 12
Training loss for the five instances.
Figure 13
Figure 13
Confusion matrix of 5 separate training instances.
Figure 13
Figure 13
Confusion matrix of 5 separate training instances.
See this image and copyright information in PMC

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References

    1. Cho S., Tromburg C., Forbes C., Tran A., Allapitan E., Fay-McClymont T., Reynolds K., Schulte F. Social adjustment across the lifespan in survivors of pediatric acute lymphoblastic leukemia (ALL): A systematic review. J. Cancer Surviv. 2022:1–17. doi: 10.1007/s11764-021-01140-5. - DOI - PubMed
    1. Sheykhhasan M., Manoochehri H., Dama P. Use of CAR T-cell for acute lymphoblastic leukemia (ALL) treatment: A review study. Cancer Gene Ther. 2022;29:1080–1096. doi: 10.1038/s41417-021-00418-1. - DOI - PMC - PubMed
    1. Yang H., Zhang H., Luan Y., Liu T., Yang W., Roberts K.G., Qian M.X., Zhang B., Yang W., Perez-Andreu V., et al. Noncoding ge-netic variation in GATA3 increases acute lymphoblastic leukemia risk through local and global changes in chromatin con-formation. Nat. Genet. 2022;54:170–179. doi: 10.1038/s41588-021-00993-x. - DOI - PMC - PubMed
    1. Khullar K., Plascak J.J., Parikh R.R. Acute lymphoblastic leukemia (ALL) in adults: Disparities in treatment intervention based on access to treatment facility. Leuk. Lymphoma. 2021;63:170–178. doi: 10.1080/10428194.2021.1975187. - DOI - PubMed
    1. Chadaga K., Prabhu S., Vivekananda B.K., Niranjana S., Umakanth S. Battling COVID-19 using machine learning: A review. Cogent Eng. 2021;8:1958666. doi: 10.1080/23311916.2021.1958666. - DOI