Automatic Detection of Malaria Parasites Using Unsupervised Techniques

  • Itishree MohantyEmail author
  • P. A. Pattanaik
  • Tripti Swarnkar
Conference paper
Part of the Lecture Notes in Computational Vision and Biomechanics book series (LNCVB, volume 30)


The focus of this paper is towards comparing the computational paradigms of two unsupervised data reduction techniques, namely Auto encoder and Self-organizing Maps. The domain of inquiry in this paper is for automatic malaria identification from blood smear images, which has a great relevance in healthcare informatics and requires a good treatment for the patients. Extensive experiments are performed using the microscopically thick blood smear image datasets. Our results reveal that the deep-learning-based Auto encoder technique is better than the Self-organizing Maps technique in terms of accuracy of 87.5%. The Auto encoder technique is computationally efficient, which may further facilitate its malaria identification in the clinical routine.


Malaria Microscopic blood smear images Self-organizing maps (SOM) Auto encoder 


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Itishree Mohanty
    • 1
    Email author
  • P. A. Pattanaik
    • 1
  • Tripti Swarnkar
    • 1
  1. 1.Department of Computer Science & EngineeringS ‘O’ A (Deemed to be University)BhubaneswarIndia

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