1. Díaz G, González FA, Romero E (2009) A semiautomatic method for quantification and classification of erythrocytes infected with malaria parasites in microscopic images[J]. J Biomed Inform 42(2):296–307
2. He K, Zhang X, Ren S et al (2016) Deep residual learning for image recognition[C]. In: Proceedings of the IEEE Conference On Computer Vision And Pattern Recognition, pp 770–778
3. Hung J, Carpenter A (2017) Applying faster r-cnn for object detection on malaria images[C]. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp 56–61
4. Jan Z, Khan A, Sajjad M et al (2018) A review on automated diagnosis of malaria parasite in microscopic blood smears images[J]. Multimed Tools Appl 77(8):9801–9826
5. Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks[C]. Adv Neural Inf Proces Syst 60:1097–1105