|
KMID : 1137820220430060441
|
|
의공학회지
2022 Volume.43 No. 6 p.441 ~ p.447
|
|
|
Pill Identification Algorithm Based on Deep Learning Using Imprinted Text Feature
|
|
Lee Seon-Min
Kim Young-Jae
Kim Kwang-Gi
|
|
|
Abstract
|
|
|
|
In this paper, we propose a pill identification model using engraved text feature and image feature such as shape and color, and compare it with an identification model that does not use engraved text feature to verify the possibility of improving identification performance by improving recognition rate of the engraved text. The data con- sisted of 100 classes and used 10 images per class. The engraved text feature was acquired through Keras OCR based on deep learning and 1D CNN, and the image feature was acquired through 2D CNN. According to the identification results, the accuracy of the text recognition model was 90%. The accuracy of the comparative model and the proposed model was 91.9% and 97.6%. The accuracy, precision, recall, and F1-score of the proposed model were better than those of the comparative model in terms of statistical significance. As a result, we confirmed that the expansion of the range of feature improved the performance of the identification model.
|
|
|
KEYWORD
|
|
|
Pill identification, Deep learning, Imprinted text, Keras OCR, CNN
|
|
|
FullTexts / Linksout information
|
|
|
|
|
|
Listed journal information
|
|
 
|