Abstract
Malaria poses a serious global health problem and it requires a rapid, accurate diagnosis to control the disease. An image processing algorithm for accurate and rapid automation in the diagnosis of malaria in blood images is developed in this research paper. The image classification system to identify the malarial parasites positively present in thin blood smears is designed, and differentiated into the various species and stages of malaria - falciparum and vivax prevalent in India. Method implemented presents a new approach to image processing in which the detection experiments employed the KNN rule, along with other algorithms such as ANN (Artificial Neural Networks), Zack’s thresholding and Linear Programming and Template matching to find out the optimal classifier for detection and classification of malarial parasites with its stages.
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Khot, S.T., Prasad, R.K. (2015). Optimal Computer Based Analysis for Detecting Malarial Parasites. In: Satapathy, S., Biswal, B., Udgata, S., Mandal, J. (eds) Proceedings of the 3rd International Conference on Frontiers of Intelligent Computing: Theory and Applications (FICTA) 2014. Advances in Intelligent Systems and Computing, vol 327. Springer, Cham. https://doi.org/10.1007/978-3-319-11933-5_9
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DOI: https://doi.org/10.1007/978-3-319-11933-5_9
Publisher Name: Springer, Cham
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