1. Sickle cell disease screening from thin blood smears using a smartphone-based microscope and deep learning
- Author
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Elizabeth Van Dyne, Derek Tseng, Yair Rivenson, Megha Ilango, Hatice Ceylan Koydemir, Kevin de Haan, Aydogan Ozcan, Lissette Bakic, Kyle Liang, Esin Gumustekin, and Doruk Karinca
- Subjects
Microscope ,Artificial neural network ,Computer science ,business.industry ,Deep learning ,Cell segmentation ,law.invention ,Blood smear ,Disease Screening ,law ,Computer vision ,Artificial intelligence ,business ,Point of care - 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.
- Published
- 2020