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20 August 2020 Sickle cell disease screening from thin blood smears using a smartphone-based microscope and deep learning
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Abstract
We report a deep learning-based framework which can be used to screen thin blood smears for sickle-cell-disease using images captured by a smartphone-based microscope. This framework first uses a deep neural network to enhance and standardize the smartphone images to the quality of a diagnostic level benchtop microscope, and a second deep neural network performs cell segmentation. We experimentally demonstrated that this technique can achieve 98% accuracy with an area-under-the-curve (AUC) of 0.998 on a blindly tested dataset made up of thin blood smears coming from 96 patients, of which 32 had been diagnosed with sickle cell disease.
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© (2020) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Kevin de Haan, Hatice C. Koydemir, Yair Rivenson, Derek Tseng, Elizabeth Van Dyne, Lissette Bakic, Doruk Karinca, Kyle Liang, Megha Ilango, Esin Gumustekin, and Aydogan Ozcan "Sickle cell disease screening from thin blood smears using a smartphone-based microscope and deep learning", Proc. SPIE 11469, Emerging Topics in Artificial Intelligence 2020, 114691M (20 August 2020); https://doi.org/10.1117/12.2567508
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