Abstract
Malaria remains a major burden on global health, causing about half a million deaths every year. The objective of this work is to develop a fast, automated, smartphone-supported malaria diagnostic system. Our proposed system is the first system using both image processing and deep learning methods on a smartphone to detect malaria parasites in thick blood smears. The underlying detection algorithm is based on an iterative method for parasite candidate screening and a convolutional neural network model (CNN) for feature extraction and classification. The system runs on Android phones and can process blood smear images taken by the smartphone camera when attached to the eyepiece of a microscope. We tested the system on 50 normal patients and 150 abnormal patients. The accuracies of the system on patch-level and patient-level are 97% and 78%, respectively. AUC values on patch-level and patient-level are, respectively, 98% and 85%. Our system could aid in malaria diagnosis in resource-limited regions, without depending on extensive diagnostic expertise or expensive diagnostic equipment.
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Acknowledgment
We would like to thank Dr. Md. A. Hossain for supporting our data acquisition at Chittagong Medical Hospital, Bangladesh. This research is supported by the Intramural Research Program of the National Institutes of Health, National Library of Medicine, and Lister Hill National Center for Biomedical Communications. Mahidol-Oxford Tropical Medicine Research Unit is funded by the Wellcome Trust of Great Britain. This research is also supported by the National Basic Research Program of China under No. 61671049 and the National Key R&D Plan of China under No. 2017YFB1400100.
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Yang, F., Yu, H., Silamut, K., Maude, R.J., Jaeger, S., Antani, S. (2019). Smartphone-Supported Malaria Diagnosis Based on Deep Learning. In: Suk, HI., Liu, M., Yan, P., Lian, C. (eds) Machine Learning in Medical Imaging. MLMI 2019. Lecture Notes in Computer Science(), vol 11861. Springer, Cham. https://doi.org/10.1007/978-3-030-32692-0_9
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DOI: https://doi.org/10.1007/978-3-030-32692-0_9
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