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Malaria Disease Detection Using CNN Technique with SGD, RMSprop and ADAM Optimizers

  • Avinash KumarEmail author
  • Sobhangi Sarkar
  • Chittaranjan Pradhan
Chapter
First Online:
Part of the Studies in Big Data book series (SBD, volume 68)

Abstract

Malaria is life-threatening disease spread when an infected female Anopheles mosquito bites a person. Malaria is one of the predominant diseases in the world. There exists many drugs which make malaria a curable disease but due to inadequate technologies and equipments, we are unable to detect and cure it. The method of diagnosing malaria involves counting of parasite and red blood cells drugs physically which is a labor-intensive and error-prone process, especially if patients have to be tested several times a day. This issue can be solved by training machines to do the work of pathologists. We can the train the machine using many deep learning algorithms. Our model uses CNN based classification to classify the blood films to infected and normal blood films. The experimental result show our model works well on microscopic image and achieves an accuracy of 96.62% and the model has a lower model complexity are requires less computation time. Thus outperforming the state of art used previously.

Keywords

Malaria CNN SGD ADAM RMSprop Deep learning 
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References

  1. 1.
    Malaria Microscopy Quality Assurance Manual, version 2. World Health Organization (2016)Google Scholar
  2. 2.
    World Malaria Report. World Health Organization (2016)Google Scholar
  3. 3.
    O’Meara, W.P., Mckenzie, F.E., Magill, A.J., Forney, J.R., Permpanich, B., Lucas, C., Gasser, R.A., Wongsrichanalai, C.: Sources of variability in determining malaria parasite density by microscopy. Am. J. Trop. Med. Hyg. 73(3), 593–598 (2005)CrossRefGoogle Scholar
  4. 4.
    Rajaraman, S., Antani, S.K., Xue, Z., Candemir, S., Jaeger, S., Thoma, G.R.: Visualizing abnormalities in chest radiographs through salient network activations in deep learning. In: Life Sciences Conference, IEEE, Australia, pp. 71–74 (2017)Google Scholar
  5. 5.
    Liang, Z., Powell, A., Ersoy, I., Poostchi, M., Silamut, K., Palaniappan, K., Guo, P., Hossain, M.A., Sameer, A., Maude, R.J., Huang, J.X., Jaeger, S., Thoma, G.: CNN-based image analysis for malaria diagnosis. In: International Conference on Bioinformatics and Biomedicine, IEEE, China, pp. 493–496 (2016)Google Scholar
  6. 6.
    Dong, Y., Jiang, Z., Shen, H., Pan, W.D., Williams, L.A., Reddy, V.V.B., Benjamin, W.H., Bryan, A.W.: Evaluations of deep convolutional neural networks for automatic identification of malaria infected cells. In: International Conference on Biomedical and Health Informatics, IEEE, USA, pp. 101–104 (2017)Google Scholar
  7. 7.
    Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, ACM, USA, pp. 2217–2225 (2016)Google Scholar
  8. 8.
    Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A.: Going deeper with convolutions. In: International Conference on Computer Vision and Pattern Recognition, IEEE, USA, pp. 1–9 (2015)Google Scholar
  9. 9.
    Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. J. Mach. Learn. Res. 13(1), 281–305 (2012)MathSciNetzbMATHGoogle Scholar
  10. 10.
  11. 11.
    Majumdar, S.: DenseNet Implementation in Keras. GitHubGoogle Scholar
  12. 12.
    Shang, F., Zhou, K., Liu, H., Cheng, J., Tsang, I.W., Zhang, L., Tao, D., Jiao, L.: VR-SGD: a simple stochastic variance reduction method for machine learning. IEEE Trans. Knowl. Data Eng. (2018)Google Scholar
  13. 13.
    Yazan, E., Talu, M.F.: Comparison of the stochastic gradient descent based optimization techniques. In: International Artificial Intelligence and Data Processing Symposium. IEEE, Turkey (2017)Google Scholar
  14. 14.
    Zhang, Z.: Improved Adam optimizer for deep neural networks. In: International Symposium on Quality of Service. IEEE, Canada (2018)Google Scholar
  15. 15.
    Gopakumar, G.P., Swetha, M., Sai Siva, G., Sai Subrahmanyam, G.R.K.: Convolutional neural network-based malaria diagnosis from focus stack of blood smear images acquired using custom-built slide scanner. J. Biophotonics 11(3) (2017)CrossRefGoogle Scholar
  16. 16.
    Saha, S.: A comprehensive guide to convolutional neural networks—the ELI5 way. Towards Data ScienceGoogle Scholar
  17. 17.
    Prabhu, R.: Understanding of convolutional neural network (CNN)—deep learningGoogle Scholar
  18. 18.
    Bibin, D., Nair, M.S., Punitha, P.: Malaria parasite detection from peripheral blood smear images using deep belief networks. IEEE, pp. 9099–9108 (2017)CrossRefGoogle Scholar
  19. 19.
    Das, D.K., Maiti, A.K., Chakraborty, C.: Automated system for characterization and classification of malaria-infected stages using light microscopic images of thin blood smears. J. Microsc. 257(3), 238–252 (2015)CrossRefGoogle Scholar
  20. 20.
    Kumar, A., Sarkar, S., Pradhan, C.: Recommendation system for crop identification and pest control technique in agriculture. In: International Conference of Communication and Signal Processing. IEEE, India, pp. 185–189 (2019)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Avinash Kumar
    • 1
    Email author
  • Sobhangi Sarkar
    • 1
  • Chittaranjan Pradhan
    • 1
  1. 1.School of Computer EngineeringKIIT DUBhubaneswarIndia

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