基于深度迁移学习增强现实移动应用的药盒识别

D. Avola, L. Cinque, Alessio Fagioli, G. Foresti, Marco Raoul Marini, Alessio Mecca, D. Pannone
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引用次数: 0

摘要

服药是治疗疾病的一个基本方面。然而,研究表明,患者很难记住正确的发音。更严重的是,错误的剂量通常会导致疾病恶化。尽管一种药物的所有相关说明都总结在相应的患者信息小册子中,但后者通常难以浏览和理解。为了解决这个问题并帮助患者用药,在本文中,我们介绍了一个增强现实移动应用程序,可以向用户展示框架药物的重要细节。特别是,该应用程序实现了基于深度神经网络(即密集网络)的推理引擎,经过微调,可以从包装中识别药物。随后,相关信息,如动物学或简化的传单,被覆盖在相机馈送,以帮助患者服用药物。在专门收集的数据集上进行了广泛的实验,以选择最佳的超参数来解决此任务;最终获得高达91.30%的准确度和实时性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Medicinal Boxes Recognition on a Deep Transfer Learning Augmented Reality Mobile Application
Taking medicines is a fundamental aspect to cure illnesses. However, studies have shown that it can be hard for patients to remember the correct posology. More aggravating, a wrong dosage generally causes the disease to worsen. Although, all relevant instructions for a medicine are summarized in the corresponding patient information leaflet, the latter is generally difficult to navigate and understand. To address this problem and help patients with their medication, in this paper we introduce an augmented reality mobile application that can present to the user important details on the framed medicine. In particular, the app implements an inference engine based on a deep neural network, i.e., a densenet, fine-tuned to recognize a medicinal from its package. Subsequently, relevant information, such as posology or a simplified leaflet, is overlaid on the camera feed to help a patient when taking a medicine. Extensive experiments to select the best hyperparameters were performed on a dataset specifically collected to address this task; ultimately obtaining up to 91.30\% accuracy as well as real-time capabilities.
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