论文标题

深度转移学习增强现实移动应用程序中的药物盒识别

Medicinal Boxes Recognition on a Deep Transfer Learning Augmented Reality Mobile Application

论文作者

Avola, Danilo, Cinque, Luigi, Fagioli, Alessio, Foresti, Gian Luca, Marini, Marco Raoul, Mecca, Alessio, Pannone, Daniele

论文摘要

服用药物是治愈疾病的基本方面。但是,研究表明,患者很难记住正确的知识。更加严重的是,错误的剂量通常会导致该疾病恶化。尽管在相应的患者信息传单中总结了所有药物的相关说明,但后者通常很难进行导航和理解。为了解决这个问题并帮助患者服药,在本文中,我们引入了一个增强的现实移动应用程序,可以向用户提供有关框架药物的重要细节。特别是,该应用程序实现了基于深神经网络的推理引擎,即经过微调的Densenet,以识别其包装中的药用。随后,相关信息,例如知识或简化的传单,被覆盖在相机饲料上,以帮助患者服药。在专门收集的数据集上执行了选择最佳超参数的广泛实验,以解决此任务;最终获得高达91.30 \%的准确性以及实时功能。

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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