论文标题
自动化医疗设备显示使用深度学习对象检测阅读
Automated Medical Device Display Reading Using Deep Learning Object Detection
论文作者
论文摘要
远程医疗和移动卫生应用，特别是在Covid-19-19大流行期间的检疫上，导致人们需要将健康监测量从患者转移到专家的需求增加。考虑到大多数家庭医疗设备都使用7段显示器，自动显示读数应为远程医疗保健提供更可靠的工具。这项工作提出了一种基于深度学习对象检测模型从医疗设备中检测和读取7段显示的端到端方法。在由MS-COCO数据集培训的两个先前培训的现状模型家族中，在由移动数码摄像头拍摄的医疗设备照片组成的数据集上进行了微调，以模拟实际案例应用程序。对受过训练的模型的评估显示出高效率，其中所有模型均获得了98％的检测精度和98％以上的分类精度，模型有效DET-LITE1均显示100％检测精度和104张图像和438位数字的测试集的100％正确数字分类。
Telemedicine and mobile health applications, especially during the quarantine imposed by the covid-19 pandemic, led to an increase on the need of transferring health monitor readings from patients to specialists. Considering that most home medical devices use seven-segment displays, an automatic display reading algorithm should provide a more reliable tool for remote health care. This work proposes an end-to-end method for detection and reading seven-segment displays 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 dataset comprised by medical devices photos taken with mobile digital cameras, to simulate real case applications. Evaluation of the trained model show high efficiency, where all models achieved more than 98% of detection precision and more than 98% classification accuracy, with model EfficientDet-lite1 showing 100% detection precision and 100% correct digit classification for a test set of 104 images and 438 digits.
