Computer-Automated Malaria Diagnosis and Quantitation Using Convolutional Neural Networks
© 2017 IEEE. The optical microscope remains a widely-used tool for diagnosis and quantitation of malaria. An automated system that can match the performance of well-trained technicians is motivated by a shortage of trained microscopists. We have developed a computer vision system that leverages deep learning to identify malaria parasites in micrographs of standard, field-prepared thick blood films. The prototype application diagnoses P. falciparum with sufficient accuracy to achieve competency level 1 in the World Health Organization external competency assessment, and quantitates with sufficient accuracy for use in drug resistance studies. A suite of new computer vision techniques-global white balance, adaptive nonlinear grayscale, and a novel augmentation scheme-underpin the system's state-of-the-art performance. We outline a rich, global training set; describe the algorithm in detail; argue for patient-level performance metrics for the evaluation of automated diagnosis methods; and provide results for P. falciparum.
Mehanian, C; Jaiswal, M; Delahunt, C; Thompson, C; Horning, M; Hu, L; McGuire, S; Ostbye, T; Mehanian, M; Wilson, B; Champlin, C; Long, E; Proux, S; Gamboa, D; Chiodini, P; Carter, J; Dhorda, M; Isaboke, D; Ogutu, B; Oyibo, W; Villasis, E; Tun, KM; Bachman, C; Bell, D
Proceedings 2017 Ieee International Conference on Computer Vision Workshops, Iccvw 2017
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International Standard Book Number 13 (ISBN-13)
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