Novel Image Processing Method for Detecting Strep Throat (Streptococcal Pharyngitis) Using Smartphone
Novel Image Processing Method for Detecting Strep Throat (Streptococcal Pharyngitis) Using Smartphone
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DOI：
10.3390/s19153307
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发表时间：
2019-08-01
期刊：
影响因子：
3.9
通讯作者：
Chong, Jo Woon
中科院分区：
文献类型：
--
作者：
Askarian, Behnam;Yoo, Seung-Chul;Chong, Jo Woon
在本文中，我们提出了一种新的链球菌咽喉检测方法，使用带有附加小工具的智能手机。我们基于智能手机的链球菌咽喉检测方法是基于使用嵌入在智能手机中的相机和手电筒。该算法使用带有小工具的智能手机获取喉咙图像，使用颜色变换和颜色校正算法处理所获取的图像，并最终使用机器学习技术将链球菌性咽炎（或链球菌）喉咙与健康喉咙分类。我们开发的小工具旨在最大限度地减少进入相机传感器的光的反射。本文的范围仅限于链球菌和健康喉咙之间的二元分类。具体来说，我们采用k折验证技术进行分类，该技术从训练集和验证集中找到最佳决策边界，并将获得的最佳决策边界应用于测试集。实验结果表明，我们提出的检测方法检测链球菌喉咙93.75%的准确性，88%的特异性，和87.5%的灵敏度平均。
In this paper, we propose a novel strep throat detection method using a smartphone with an add-on gadget. Our smartphone-based strep throat detection method is based on the use of camera and flashlight embedded in a smartphone. The proposed algorithm acquires throat image using a smartphone with a gadget, processes the acquired images using color transformation and color correction algorithms, and finally classifies streptococcal pharyngitis (or strep) throat from healthy throat using machine learning techniques. Our developed gadget was designed to minimize the reflection of light entering the camera sensor. The scope of this paper is confined to binary classification between strep and healthy throats. Specifically, we adopted k-fold validation technique for classification, which finds the best decision boundary from training and validation sets and applies the acquired best decision boundary to the test sets. Experimental results show that our proposed detection method detects strep throats with 93.75% accuracy, 88% specificity, and 87.5% sensitivity on average.