Abstract
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.
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Mohanty, I., Pattanaik, P.A., Swarnkar, T. (2019). Automatic Detection of Malaria Parasites Using Unsupervised Techniques. In: Pandian, D., Fernando, X., Baig, Z., Shi, F. (eds) Proceedings of the International Conference on ISMAC in Computational Vision and Bio-Engineering 2018 (ISMAC-CVB). ISMAC 2018. Lecture Notes in Computational Vision and Biomechanics, vol 30. Springer, Cham. https://doi.org/10.1007/978-3-030-00665-5_5
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DOI: https://doi.org/10.1007/978-3-030-00665-5_5
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