7 March 2017 A survey of supervised machine learning models for mobile-phone based pathogen identification and classification
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Abstract
Giardia lamblia causes a disease known as giardiasis, which results in diarrhea, abdominal cramps, and bloating. Although conventional pathogen detection methods used in water analysis laboratories offer high sensitivity and specificity, they are time consuming, and need experts to operate bulky equipment and analyze the samples. Here we present a field-portable and cost-effective smartphone-based waterborne pathogen detection platform that can automatically classify Giardia cysts using machine learning. Our platform enables the detection and quantification of Giardia cysts in one hour, including sample collection, labeling, filtration, and automated counting steps. We evaluated the performance of three prototypes using Giardia-spiked water samples from different sources (e.g., reagent-grade, tap, non-potable, and pond water samples). We populated a training database with >30,000 cysts and estimated our detection sensitivity and specificity using 20 different classifier models, including decision trees, nearest neighbor classifiers, support vector machines (SVMs), and ensemble classifiers, and compared their speed of training and classification, as well as predicted accuracies. Among them, cubic SVM, medium Gaussian SVM, and bagged-trees were the most promising classifier types with accuracies of ~ 94.1%, 94.2%, and 95%, respectively; we selected the latter as our preferred classifier for the detection and enumeration of Giardia cysts that are imaged using our mobile-phone fluorescence microscope. Without the need for any experts or microbiologists, this field-portable pathogen detection platform can present a useful tool for water quality monitoring in resource-limited-settings.
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Hatice Ceylan Koydemir, Hatice Ceylan Koydemir, Steve Feng, Steve Feng, Kyle Liang, Kyle Liang, Rohan Nadkarni, Rohan Nadkarni, Derek Tseng, Derek Tseng, Parul Benien, Parul Benien, Aydogan Ozcan, Aydogan Ozcan, } "A survey of supervised machine learning models for mobile-phone based pathogen identification and classification", Proc. SPIE 10055, Optics and Biophotonics in Low-Resource Settings III, 100550A (7 March 2017); doi: 10.1117/12.2251517; https://doi.org/10.1117/12.2251517
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