Evaluations of Deep Convolutional Neural Networks for Automatic Identification of Malaria Infected Cells

Yuhang Dong1, Zhuocheng Jiang1, Hongda Shen1, Wendi Pan1, Lance Williams2, Vishnu Reddy2, William Benjamin2, Bryan Allen2

  • 1University of Alabama in Huntsville
  • 2University of Alabama at Birmingham



Contributed Paper (Oral)


5. Imaging Informatics


16:00 - 17:30 | Thu 16 Feb | Salon 5 | ThC1

Thu1.3: Imaging Informatics


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 over 95%, higher than the accuracy of about 92% attainable by using the support vector machine method. Moreover, the deep learning methods have the advantage of being able to automatically learn the features from the input data, thereby requiring minimal inputs from human experts for automated malaria diagnosis.

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