Department of Electrical and Computer Engineering, Texas Tech University, Lubbock, TX 79409, USA.
School of Communication & Media, Ewha Womans University, Seoul 03760, Korea.
Sensors (Basel). 2019 Jul 27;19(15):3307. doi: 10.3390/s19153307.
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 -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.
在本文中，我们提出了一种使用带有附加小工具的智能手机来检测链球菌性咽炎的新方法。我们基于智能手机的链球菌性咽炎检测方法是基于利用智能手机中嵌入的摄像头和闪光灯。所提出的算法使用带有小工具的智能手机获取喉部图像，使用颜色变换和颜色校正算法处理获取的图像，最后使用机器学习技术从健康的喉部中分类链球菌性咽炎（或链球菌）。我们开发的小工具旨在最大程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性咽炎和健康喉部之间的二分类。具体来说，我们采用了-折验证技术进行分类，该技术从训练集和验证集找到最佳决策边界，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平均可以以 93.75%的准确率、88%的特异性和 87.5%的敏感性来检测链球菌性咽炎。
