Soft computing to determine a Hemoglobin level of an early stage Multiple Myeloma patient by using Rectified Linear Units (ReLu) activation function

Authors

  • Mishra A Center for Artificial Intelligence and Friction Stir Welding, Stir Research Technologies, Uttar Pradesh, India
  • Diwan M Department of Computer Science, University of Petroleum and Energy Studies, Dehradun, India

DOI:

https://doi.org/10.26438/ijcse/v7i9.2630

Keywords:

Multiple Myeloma, Artificial Intelligence, Artificial Neural Network, Hemoglobin level

Abstract

Artificial Intelligence (AI) has found various applications in many industries, from development of new alloys to cyber security and healthcare domain. By 2025 it is expected that the market for healthcare artificial intelligence tools will surpass 34 billion dollars. There is no doubt that the application of AI is going to lead to a real digital shift in traditional medical imaging, requiring AI and people to work together to meet the challenges of the medical industry. In our present work, we have tried to determine the hemoglobin level corresponding to Packed Cell Volume (PCV) and Red Blood Cells (RBC) count. In the Artificial Neural Network (ANN) architecture, PCV (%) and RBC count (mill/cumm) are the inputs while hemoglobin (g/dL) is the output. The result obtained is quite promising. Artificial Neural Network (ANN) trained on Rectified Linear Unit (ReLu) activation function showed 97.15% accuracy.

References

[1] Patel, V.L., Shortliffe, E.H., Stefanelli, M., Szolovits, P., Berthold, M.R., Bellazzi, R. and Abu-Hanna, A., 2009. The coming of age of artificial intelligence in medicine. Artificial intelligence in medicine, 46(1), pp.5-17.

[2] Jaremko, Jacob L. et al. Canadian Association of Radiologists Journal, Volume 70, Issue 2, 107 – 118

[3] Soni, J., Ansari, U., Sharma, D. and Soni, S., 2011. Predictive data mining for medical diagnosis: An overview of heart disease prediction. International Journal of Computer Applications, 17(8), pp.43-48.

[4] Lo, S.C., Lou, S.L., Lin, J.S., Freedman, M.T., Chien, M.V. and Mun, S.K., 1995. Artificial convolution neural network techniques and applications for lung nodule detection. IEEE Transactions on Medical Imaging, 14(4), pp.711-718.

Downloads

Published

2019-09-30
CITATION
DOI: 10.26438/ijcse/v7i9.2630
Published: 2019-09-30

How to Cite

[1]
A. Mishra and M. Diwan, “Soft computing to determine a Hemoglobin level of an early stage Multiple Myeloma patient by using Rectified Linear Units (ReLu) activation function”, Int. J. Comp. Sci. Eng., vol. 7, no. 9, pp. 26–30, Sep. 2019.

Issue

Vol. 7 No. 9 (2019): IJCSE Sept. Edition

Section

Research Article

License

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Authors contributing to this journal agree to publish their articles under the Creative Commons Attribution 4.0 International License, allowing third parties to share their work (copy, distribute, transmit) and to adapt it, under the condition that the authors are given credit and that in the event of reuse or distribution, the terms of this license are made clear.