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.
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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