1. Y. Dong, Z. Jiang, H. Shen, W.D. Pan, Classification accuracies of malaria infected cells using deep convolutional neural networks based on decompressed images. SoutheastCon 2017, 1–6 (2017)
2. S. Shuleenda Devi, S. Alam Sheikh, R. Hussain Laskar, Erythrocyte features for malaria parasite detection in microscopic images of thin blood smear: a review. Int. J. Interact. Multimed. Artif. Intel. 4(2), 34 (2016). Available http://www.ijimai.org/journal/node/1442
3. J.A. Quinn, R. Nakasi, P.K.B. Mugagga, P. Byanyima, W. Lubega, A. Andama, Deep convolutional neural networks for microscopy-based point of care diagnostics, in Machine Learning and Healthcare Conference (MLHC 2016), vol. 56 (2016). Available http://arxiv.org/abs/1608.02989
4. S.P. Premaratne, N.D. Karunaweera, S. Fernando, A neural network architecture for automated recognition of intracellular malaria parasites in stained blood films (2006), pp. 4–7
5. K.E.D. Peñas, P.T. Rivera, P.C. Naval, Malaria parasite detection and species identification on thin blood smears using a convolutional neural network, in 2017 IEEE/ACM International Conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE) (2017), pp. 1–6