Medical tablet identification algorithm
Tablet misidentification possesses hazardous threats to medication safety. Incorrect medications could potentially result in adverse drug effect (AVD), which could even lead to death in long-term consumption. Research has shown that nine out of ten US citizens over the age of 65, who take more than...
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| Format: | Final Year Project (FYP) |
| Language: | English |
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Nanyang Technological University
2020
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| Online Access: | https://hdl.handle.net/10356/140951 |
| _version_ | 1826113507734585344 |
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| author | Lee, Jia Yi |
| author2 | Ser Wee |
| author_facet | Ser Wee Lee, Jia Yi |
| author_sort | Lee, Jia Yi |
| collection | NTU |
| description | Tablet misidentification possesses hazardous threats to medication safety. Incorrect medications could potentially result in adverse drug effect (AVD), which could even lead to death in long-term consumption. Research has shown that nine out of ten US citizens over the age of 65, who take more than one prescription tablets, are likely to misidentify their tablets [1]. As there is an increasing type of medicines and pharmaceutical brands being marketed in addition to the thousands already available, many of these medications may have a very similar appearance to each other. The look-alike tablet presents a challenging task for people to identify them correctly, especially for the elderly. Under the worst-case scenario, clinical testing is required to identify the components of the unknown tablet to know its actual identity. This project aims to develop an image-based tablet identification algorithm using computer vision approaches. Four attributes being utilized are shape, colour, size and imprint. The algorithm developed will identify and show the top five “visually similar” tablets together with the medical name of these tablets and the corresponding similarity scores. A collection of approximately 300 tablet images has been provided by Tan Tock Seng hospital to support the development of this project. Matric Laboratory (MATLAB) is the software platform used to develop the algorithm. |
| first_indexed | 2024-10-01T03:24:22Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/140951 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T03:24:22Z |
| publishDate | 2020 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-10356/1409512023-07-07T16:25:12Z Medical tablet identification algorithm Lee, Jia Yi Ser Wee School of Electrical and Electronic Engineering ewser@ntu.edu.sg Engineering::Electrical and electronic engineering Tablet misidentification possesses hazardous threats to medication safety. Incorrect medications could potentially result in adverse drug effect (AVD), which could even lead to death in long-term consumption. Research has shown that nine out of ten US citizens over the age of 65, who take more than one prescription tablets, are likely to misidentify their tablets [1]. As there is an increasing type of medicines and pharmaceutical brands being marketed in addition to the thousands already available, many of these medications may have a very similar appearance to each other. The look-alike tablet presents a challenging task for people to identify them correctly, especially for the elderly. Under the worst-case scenario, clinical testing is required to identify the components of the unknown tablet to know its actual identity. This project aims to develop an image-based tablet identification algorithm using computer vision approaches. Four attributes being utilized are shape, colour, size and imprint. The algorithm developed will identify and show the top five “visually similar” tablets together with the medical name of these tablets and the corresponding similarity scores. A collection of approximately 300 tablet images has been provided by Tan Tock Seng hospital to support the development of this project. Matric Laboratory (MATLAB) is the software platform used to develop the algorithm. Bachelor of Engineering (Electrical and Electronic Engineering) 2020-06-03T03:32:21Z 2020-06-03T03:32:21Z 2020 Final Year Project (FYP) https://hdl.handle.net/10356/140951 en A3197-191 application/pdf Nanyang Technological University |
| spellingShingle | Engineering::Electrical and electronic engineering Lee, Jia Yi Medical tablet identification algorithm |
| title | Medical tablet identification algorithm |
| title_full | Medical tablet identification algorithm |
| title_fullStr | Medical tablet identification algorithm |
| title_full_unstemmed | Medical tablet identification algorithm |
| title_short | Medical tablet identification algorithm |
| title_sort | medical tablet identification algorithm |
| topic | Engineering::Electrical and electronic engineering |
| url | https://hdl.handle.net/10356/140951 |
| work_keys_str_mv | AT leejiayi medicaltabletidentificationalgorithm |