D. Avola, L. Cinque, Alessio Fagioli, G. Foresti, Marco Raoul Marini, Alessio Mecca, D. Pannone
{"title":"基于深度迁移学习增强现实移动应用的药盒识别","authors":"D. Avola, L. Cinque, Alessio Fagioli, G. Foresti, Marco Raoul Marini, Alessio Mecca, D. Pannone","doi":"10.48550/arXiv.2203.14031","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":74527,"journal":{"name":"Proceedings of the ... International Conference on Image Analysis and Processing. International Conference on Image Analysis and Processing","volume":"75 1","pages":"489-499"},"PeriodicalIF":0.0000,"publicationDate":"2022-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Medicinal Boxes Recognition on a Deep Transfer Learning Augmented Reality Mobile Application\",\"authors\":\"D. Avola, L. Cinque, Alessio Fagioli, G. Foresti, Marco Raoul Marini, Alessio Mecca, D. Pannone\",\"doi\":\"10.48550/arXiv.2203.14031\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"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.\",\"PeriodicalId\":74527,\"journal\":{\"name\":\"Proceedings of the ... International Conference on Image Analysis and Processing. International Conference on Image Analysis and Processing\",\"volume\":\"75 1\",\"pages\":\"489-499\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-03-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the ... International Conference on Image Analysis and Processing. International Conference on Image Analysis and Processing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.48550/arXiv.2203.14031\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the ... International Conference on Image Analysis and Processing. International Conference on Image Analysis and Processing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.48550/arXiv.2203.14031","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
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.