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Telemedicine and mobile health applications, especially during the quarantineimposed by the covid-19 pandemic, led to an increase on the need oftransferring health monitor readings from patients to specialists. Consideringthat most home medical devices use seven-segment displays, an automatic displayreading algorithm should provide a more reliable tool for remote health care.This work proposes an end-to-end method for detection and reading seven-segmentdisplays from medical devices based on deep learning object detection models.Two state of the art model families, EfficientDet and EfficientDet-lite,previously trained with the MS-COCO dataset, were fine-tuned on a datasetcomprised by medical devices photos taken with mobile digital cameras, tosimulate real case applications. Evaluation of the trained model show highefficiency, where all models achieved more than 98% of detection precision andmore than 98% classification accuracy, with model EfficientDet-lite1 showing100% detection precision and 100% correct digit classification for a test setof 104 images and 438 digits.

Automated Medical Device Display Reading Using Deep Learning Object Detection