Sickle cell disease screening from thin blood smears using a smartphone-based microscope and deep learning
Kevin d. Haan, Hatice Ceylan Koydemir, Yair Rivenson, Derek K. Tseng, Elizabeth A. Van Dyne, Lissette Bakic, Doruk Karinca, Kyle Liang, Megha Ilango, Esin Gumustekin, Aydogan Özcan · 2020
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.