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• Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. MIT Press, Cambridge (2016)
• Górriz, M., et al.: Leishmaniasis parasite segmentation and classification using deep learning. In: Perales, F.J., Kittler, J. (eds.) AMDO 2018. LNCS, vol. 10945, pp. 53–62. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-94544-6 6
• He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
• Faria, J.R.N.: Automatic detection of trypanosomes on the blood stream. Ph.D. thesis, University of Porto (2016)
• Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. CoRR abs/1412.6980 (2014)
• Krig, S.: Computer Vision Metrics. Survey, Taxonomy, and Analysis. Apress, Berkeley (2014). https://doi.org/10.1007/978-1-4302-5930-5
• Ojeda-Pat, A., Martin-Gonzalez, A., Soberanis-Mukul, R.: Convolutional neural network U-Net for Trypanosoma cruzi segmentation. In: Brito-Loeza, C., Espinosa-Romero, A., Martin-Gonzalez, A., Safi, A. (eds.) ISICS 2020. CCIS, vol. 1187, pp. 118–131. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-43364-2 11
• Paszke, A., et al.: Automatic differentiation in PyTorch (2017)
• Radwanska, M., Vereecke, N., Deleeuw, V., Pinto, J., Magez, S.: Salivarian Try-panosomosis: a review of parasites involved, their global distribution and their interaction with the innate and adaptive mammalian host immune system. Front. Immunol. 9, 2253 (2018)
• Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4 28
• Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Learning representations by back-propagating errors. Nature 323(6088), 533–536 (1986)
• Savkare, S.S., Narote, S.P., et al.: Automatic detection of malaria parasites for estimating Parasitemia. Int. J. Comput. Sci. Secur. (IJCSS) 5(3), 310–315 (2011)
• Soberanis-Mukul, R., Uc-Cetina, V., Brito-Loeza, C., Ruiz-Piña, H.: An automatic algorithm for the detection of Trypanosoma Cruzi parasites in blood sample images. Comput. Methods Programs Biomed. 112(3), 633–639 (2013)
• Stevens, E., Antiga, L., Viehmann, T.: Deep learning with PyTorch essential excerpts. Manning (2019)
• Steverding, D.: The development of drugs for treatment of sleeping sickness: a historical review. Parasites Vectors 3, 15 (2010). https://doi.org/10.1186/1756-3305-3-15
• Takagi, Y., Nosato, H., Doi, M., Furukawa, K., Sakanashi, H.: Development of a motion-based cell-counting system for Trypanosoma parasite using a pattern recognition approach. Biotechniques 66(4), 179–185 (2019)
• The Editors of Encyclopaedia Britannica: sleeping sickness (2020). https://www. britannica.com/science/sleeping-sickness
• Vilchez, C.: Examination of the peripheral blood film and correlation with the complete blood count. Rodak’s Hematology-E-Book: Clinical Principles and Applications, pp. 201–205 (2019)
• Yang, F., Poostchi, M., Yu, H., Zhou, Z., Silamut, K., Yu, J., Maude, R.J., Jaeger, S., Antani, S.: Deep learning for smartphone-based malaria parasite detection in thick blood smears. IEEE J. Biomed. Health Inform. 24(5), 1427–1438 (2019)