| dc.contributor.author | Ceh-Varela, Eduardo | |
| dc.contributor.author | Hernández-Chan, Gandhi | |
| dc.contributor.author | Villanueva-Escalante, Marisol | |
| dc.contributor.author | Alor-Hernández, Giner | |
| dc.contributor.author | Sánchez-Cervantes, José Luis | |
| dc.date.accessioned | 2021-07-07T16:39:38Z | |
| dc.date.available | 2021-07-07T16:39:38Z | |
| dc.date.issued | 2017-05-05 | |
| dc.identifier.issn | 1870-4069 | |
| dc.identifier.other | http://dx.doi.org/10.13053/rcs-132-1-1 | |
| dc.identifier.uri | http://repositorios.orizaba.tecnm.mx:8080/xmlui/handle/123456789/499 | |
| dc.description | Self-medication and self-prescription are common practices that can be observed in many countries around the world, from the most advanced in terms of medical services in Europe to the less ones as in South America or Africa. Self-medication is defined as the consumption of one or more drugs without the advice of a physician. Many studies in Mexico reveal the type of medications that are consumed as well the social groups that normally use this practice. The consequences for this can range from a mild allergic reaction to death. On the other hand, it is easy to buy drugs without a prescription in pharmacies or
supermarkets, but consumers do not always know which one to choose, neither the ingredients nor side effects they can cause. Here we present a classifier model for counter medication based on computer vision and machine learning techniques. We collected 150 images from 11 different counter medications. The classifier was tested with 43 new images, and obtained 90.7% of accuracy, 93% of precision, 91% of recall and 91% of F1-score. | es |
| dc.description.abstract | Self-medication and self-prescription are common practices that can be observed in many countries around the world, from the most advanced in terms of medical services in Europe to the less ones as in South America or Africa. Self-medication is defined as the consumption of one or more drugs without the advice of a physician. Many studies in Mexico reveal the type of medications that are consumed as well the social groups that normally use this practice. The consequences for this can range from a mild allergic reaction to death. On the other hand, it is easy to buy drugs without a prescription in pharmacies or
supermarkets, but consumers do not always know which one to choose, neither the ingredients nor side effects they can cause. Here we present a classifier model for counter medication based on computer vision and machine learning techniques. We collected 150 images from 11 different counter medications. The classifier was tested with 43 new images, and obtained 90.7% of accuracy, 93% of precision, 91% of recall and 91% of F1-score. | es |
| dc.language.iso | en_US | es |
| dc.publisher | Instituto Politécnico Nacional | es |
| dc.relation.ispartofseries | Research in Computing Science; | |
| dc.subject | Self-medication | es |
| dc.subject | computer vision | es |
| dc.subject | machine learning | es |
| dc.title | A Counter Medication Classifier Using Machine Learning and Computer Vision Techniques | es |
| dc.type | Article | es |
