Automatic Detection of Malaria Parasites Using Unsupervised Techniques

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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