Department of Pharmacy, Faculty of Medicine, University of Malaya, Malaysia.
Department of Pharmacy, Tuen Moon Hospital, Hong Kong.
J Biomed Inform. 2017 Oct;74:130-136. doi: 10.1016/j.jbi.2017.09.005. Epub 2017 Sep 15.
Oral pills, including tablets and capsules, are one of the most popular pharmaceutical dosage forms available. Compared to other dosage forms, such as liquid and injections, oral pills are very stable and are easy to be administered. However, it is not uncommon for pills to be misidentified, be it within the healthcare institutes or after the pills were dispensed to the patients. Our objective is to develop groundwork for automatic pill identification and verification using Deep Convolutional Network (DCN) that surpasses the existing methods.
A DCN model was developed using pill images captured with mobile phones under unconstraint environments. The performance of the DCN model was compared to two baseline methods of hand-crafted features.
The DCN model outperforms the baseline methods. The mean accuracy rate of DCN at Top-1 return was 95.35%, whereas the mean accuracy rates of the two baseline methods were 89.00% and 70.65%, respectively. The mean accuracy rates of DCN for Top-5 and Top-10 returns, i.e., 98.75% and 99.55%, were also consistently higher than those of the baseline methods.
The images used in this study were captured at various angles and under different level of illumination. DCN model achieved high accuracy despite the suboptimal image quality.
The superior performance of DCN underscores the potential of Deep Learning model in the application of pill identification and verification.
口服片剂，包括片剂和胶囊，是最受欢迎的药物剂型之一。与其他剂型相比，如液体和注射剂，口服片剂非常稳定，易于给药。然而，药丸被错误识别的情况并不少见，无论是在医疗机构内还是在将药丸分发给患者之后。我们的目标是开发使用深度卷积网络（DCN）的自动药丸识别和验证的基础，该方法优于现有方法。
使用在非约束环境下用手机拍摄的药丸图像开发了 DCN 模型。将 DCN 模型的性能与两种手工制作特征的基线方法进行了比较。
DCN 模型优于基线方法。DCN 在 Top-1 返回的平均准确率为 95.35%，而两种基线方法的平均准确率分别为 89.00%和 70.65%。DCN 在 Top-5 和 Top-10 返回的平均准确率（即 98.75%和 99.55%）也始终高于基线方法。
本研究中使用的图像是在不同角度和不同光照水平下拍摄的。尽管图像质量不理想，DCN 模型仍实现了高精度。
DCN 的优异性能突出了深度学习模型在药丸识别和验证应用中的潜力。
