Evaluations of deep convolutional neural networks for automatic identification of malaria infected cells

@article{Dong2017EvaluationsOD,
  title={Evaluations of deep convolutional neural networks for automatic identification of malaria infected cells},
  author={Yuhang Dong and Zhuocheng Jiang and Hongda Shen and W. David Pan and Lance A. Williams and Vishnu V. B. Reddy and William H. Benjamin and Allen W. Bryan},
  journal={2017 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI)},
  year={2017},
  pages={101-104}
}
This paper studied automatic identification of malaria infected cells using deep learning methods. We used whole slide images of thin blood stains to compile an dataset of malaria-infected red blood cells and non-infected cells, as labeled by a group of four pathologists. We evaluated three types of well-known convolutional neural networks, including the LeNet, AlexNet and GoogLeNet. Simulation results showed that all these deep convolution neural networks achieved classification accuracies of… CONTINUE READING

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