The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.
CNN-based image analysis for malaria diagnosis
Abstract
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%).
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Cites
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What is the best multi-stage architecture for object recognition?
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Global malaria mortality between 1980 and 2010: a systematic analysis
Murray, C. J., Rosenfeld, L. C., Lim, S. S., Andrews, K. G., Foreman, K. J., Haring, D., Fullman, N., Naghavi, M., Lozano, R., & Lopez, A. D. (2012) · The Lancet · doi:10.1016/s0140-6736(12)60034-8 · Cited by 1,452
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Cited by
Rajaraman, S., Antani, S. K., Poostchi, M., Silamut, K., Hossain, M. A., Maude, R. J., Jaeger, S., & Thoma, G. R. (2018) · PeerJ · doi:10.7717/peerj.4568 · Cited by 566
Self-citationImage analysis and machine learning for detecting malaria
Poostchi, M., Silamut, K., Maude, R. J., Jaeger, S., & Thoma, G. (2018) · Translational Research · doi:10.1016/j.trsl.2017.12.004 · Cited by 559
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Metadata
- Authors
- Liang, Z., Powell, A., Ersoy, I., Poostchi, M., Silamut, K., Palaniappan, K., Guo, P., Hossain, M. A., Sameer, A., Maude, R. J., Huang, J. X., Jaeger, S., & Thoma, G.
- Venue
- 2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
- Publisher
- IEEE
- Published
- 2016-12
- Pages
- 493-496
- OpenAlex
- W2576442780
- Cited by
- 325
- References
- 8
- FWCI
- 6.23
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Liang, Z., Powell, A., Ersoy, I., Poostchi, M., Silamut, K., Palaniappan, K., Guo, P., Hossain, M. A., Sameer, A., Maude, R. J., Huang, J. X., Jaeger, S., & Thoma, G. (2016). CNN-based image analysis for malaria diagnosis. 2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). https://doi.org/10.1109/bibm.2016.7822567
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