Publication: CNN-based image analysis for malaria diagnosis
Issued Date
2017-01-17
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2-s2.0-85013270066
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Mahidol University
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SCOPUS
Bibliographic Citation
Proceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016. (2017), 493-496
Suggested Citation
Zhaohui Liang, Andrew Powell, Ilker Ersoy, Mahdieh Poostchi, Kamolrat Silamut, Kannappan Palaniappan, Peng Guo, Md Amir Hossain, Antani Sameer, Richard James Maude, Jimmy Xiangji Huang, Stefan Jaeger, George Thoma CNN-based image analysis for malaria diagnosis. Proceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016. (2017), 493-496. doi:10.1109/BIBM.2016.7822567 Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/41984
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Title
CNN-based image analysis for malaria diagnosis
Abstract
© 2016 IEEE. 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 technicians. 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 before. 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 microscope 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%).