Simplified Stem Cell Differential: An Inexpensive Way of Classifying Type and Stage of Cancer

Authors

  • Grout S. Assistant Professor, Department of ECE, SRMIST, Chennai, India. Author
  • Anwesha Mukherjee B. Tech, Electronics and Communication, SRMIST, Chennai, India. Author
  • Naveen Narra B. Tech, Electronics and Communication, SRMIST, Chennai, India. Author
  • Sakamuri Ramkishore B. Tech, Electronics and Communication, SRMIST, Chennai, India. Author
  • Ramani P. Assistant Professor, Department of ECE, SRMIST, Chennai, India. Author
  • Surjatapa Dutta B. Tech, Electronics and Communication, SRMIST, Chennai, India. Author

DOI:

https://doi.org/10.61841/7nwrz909

Keywords:

Anemia, Feature Extraction, Gray Level Co-Occurrence Matrix, k-means Clustering, Lab Color Space Conversion, Leukaemia, Local Binary Pattern, Probabilistic Neural Networks, Cielab Colour Space Conversion

Abstract

An automatic approach for stem cell detection and classification of diseases that happen to stem cells (RBC and WBC) is proposed. The proposed work comprises planning and creating an automated framework that will help the clinical experts in precisely distinguishing the sort and sub-kinds of the ailment. This strategy can be viably utilized in any asset-poor condition by undeveloped individuals. Right now we have taken minuscule blood images from a smartphone-microscope and are cautiously preprocessing them to set them up for highlight extraction and further order. Notwithstanding this, we have utilized four AI calculations to be specific: lab shading space change, fluffy grouping, enlightening strong nearby paired examples, dim level co-event lattice, and probabilistic neural systems. After exhaustive perception, it is noticed that PNN works better to recognize and order foundational cells liable for leukemic malignancy. Combining the highlights separated from the middle of the road layers, our methodology can possibly improve the general order execution. This mechanized leukemia recognition framework is seen as progressively more compelling, quick, precise, and perfect than manual diagnosing strategies. 

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References

[1] https://cancerimagingarchive.net/datascope/cptac/home/?filter=

[2] R. Agrawal, S. Satapathy, G. Bagla and K. Rajakumar, "Detection of White Blood Cell Cancer using Image

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[4] V. J. Ramya and S. Lakshmi, "A Methodical Review of Diagnosis and Classification of Leukemia using Blood Microscopic Images," 2019 International Conference on Communication and Signal Processing (ICCSP), Chennai, India, 2019, pp. 0258-0262.

[5] M. N. Q. Bhuiyan, S. K. Rahut, R. A. Tanvir, and S. Ripon, "Automatic Acute Lymphoblastic Leukemi Detection and Comparative Analysis from Images," 2019 6th International Conference on Control, Decision and Information Technologies (CoDIT), Paris, France, 2019, pp. 1144-1149.

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[7] https://www.lls.org/leukemia

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Published

31.07.2020

Issue

Vol. 24 No. 5 (2020): 24 (5) 2020

Section

Articles

License

Copyright (c) 2020 AUTHOR

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How to Cite

S. , G., Mukherjee, A., Narra, N., Ramkishore, S., P. , R., & Dutta, S. (2020). Simplified Stem Cell Differential: An Inexpensive Way of Classifying Type and Stage of Cancer. International Journal of Psychosocial Rehabilitation, 24(5), 363-371. https://doi.org/10.61841/7nwrz909