#241:
Z. Liang,
S. Jaeger,
G. Thoma,
J. Huang,
P. Guo,
A. Powell,
K. Silamut,
I. Ersoy,
M. Poostchi,
K. Palaniappan, and
R. J. Maude
Proc. IEEE International Conference on Bioinformatics and Biomedicine (BIBM),
2016
Abstract,
Bibtex,
PlainText,
Google Scholar
Malaria is a major global health threat. The
standard way of diagnosing malaria is by visually examining blood
smears for parasite
-
infected red blood cells under the microscope
by qualified technician
s. This method is inefficient and the
diagnosis depends on the experience and the knowledge of the
person doing the examination. Automatic image recognition
technologies based on machine learning have been applied to
malaria blood smears for diagnosis befo
re. However, the practical
performance has not been sufficient so far. This study proposes a
new and robust machine learning model based on a convolutional
neural network (CNN) to automatically classify single cells in thin
blood smears on standard microsc
ope slides as either infected or
uninfected. In a ten
-
fold cross
-
validation based on 27,578 single
cell images, the average accuracy of our new 16
-
layer CNN model
is 97.37%. A transfer learning model only achieves 91.99% on the
same images. The CNN model
shows superiority over the transfer
learning model in all performance indicators such as sensitivity
(96.99% vs 89.00%), specificity (97.75% vs 94.98%), precision
(97.73% vs 95.12%), F1 score (97.36% vs 90.24%), and Matthews
correlation coefficient (94.75%
vs 85.25%).
@inproceedings{2016a,
author = "Z. Liang and S. Jaeger and G. Thoma and J. Huang and P. Guo and A. Powell and K. Silamut and I. Ersoy and M. Poostchi and K. Palaniappan and R. J. Maude",
title = "CNN - Based Image Analysis for Malaria Diagnosis",
year = 2016,
journal = "Proc. IEEE International Conference on Bioinformatics and Biomedicine (BIBM)",
month = "Dec",
keywords = "deep learning, malaira, cells, classification, biomedical"
}
Z. Liang, S. Jaeger, G. Thoma, J. Huang, P. Guo, A. Powell, K. Silamut, I. Ersoy, M. Poostchi, K. Palaniappan, and R. J. Maude. CNN - Based Image Analysis for Malaria Diagnosis. Proc. IEEE International Conference on Bioinformatics and Biomedicine (BIBM), December 2016.