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5 March 2021 Screening of sickle cell disease using a smartphone-based microscope and deep-learning
Kevin De Haan, Hatice Ceylan Koydemir, Yair Rivenson, Derek Tseng, Elizabeth Van Dyne, Lissette Bakic, Doruk Karinca, Kyle Liang, Megha Ilango, Esin Gumustekin, Aydogan Ozcan
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Proceedings Volume 11632, Optics and Biophotonics in Low-Resource Settings VII; 116320B (2021) https://doi.org/10.1117/12.2579425
Event: SPIE BiOS, 2021, Online Only
Abstract
We present a deep-learning based device to perform automated screening of sickle cell disease (SCD) using images of blood smears captured by a smartphone-based microscope. We experimentally validated the system using 96 blood smears (including 32 positive samples for SCD), each coming from a unique patient. Tested on these blood smears, our framework achieved a 98% accuracy and had an area-under-the-curve (AUC) of 0.998. Since this technique is both low-cost and accurate, it has the potential to improve access to cost-effective screening and monitoring of patients in low resource settings – particularly in areas where existing diagnostic methods are unsuitable.
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Kevin De Haan, Hatice Ceylan Koydemir, Yair Rivenson, Derek Tseng, Elizabeth Van Dyne, Lissette Bakic, Doruk Karinca, Kyle Liang, Megha Ilango, Esin Gumustekin, and Aydogan Ozcan "Screening of sickle cell disease using a smartphone-based microscope and deep-learning", Proc. SPIE 11632, Optics and Biophotonics in Low-Resource Settings VII, 116320B (5 March 2021); https://doi.org/10.1117/12.2579425
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KEYWORDS
Microscopes
Single crystal X-ray diffraction
Blood
Image segmentation
Neural networks
Diagnostics
Image classification
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