Abstract
The gold standard for malaria diagnosis still remains to be microscopy. However, cases from remote areas needing immediate diagnosis and treatment can benefit from a faster diagnostic process. Several intelligent systems for malaria diagnosis have been proposed using different computer vision techniques. In this research, models using convolutional neural networks, and a model using extracted shape features are implemented and compared. The CNN models, one trained from scratch and the other utilizing transfer learning, with accuracies of 92.4% and 93.60%, both outperform the shape feature model in malaria parasite recognition.
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Delas Peñas, K., Rivera, P.T., Naval, P.C. (2018). Analysis of Convolutional Neural Networks and Shape Features for Detection and Identification of Malaria Parasites on Thin Blood Smears. In: Nguyen, N., Hoang, D., Hong, TP., Pham, H., Trawiński, B. (eds) Intelligent Information and Database Systems. ACIIDS 2018. Lecture Notes in Computer Science(), vol 10752. Springer, Cham. https://doi.org/10.1007/978-3-319-75420-8_45
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DOI: https://doi.org/10.1007/978-3-319-75420-8_45
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