{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,5]],"date-time":"2026-06-05T23:07:27Z","timestamp":1780700847029,"version":"3.54.1"},"reference-count":51,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2019,7,27]],"date-time":"2019-07-27T00:00:00Z","timestamp":1564185600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/s19153307","type":"journal-article","created":{"date-parts":[[2019,7,29]],"date-time":"2019-07-29T03:06:58Z","timestamp":1564369618000},"page":"3307","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":36,"title":["Novel Image Processing Method for Detecting Strep Throat (Streptococcal Pharyngitis) Using Smartphone"],"prefix":"10.3390","volume":"19","author":[{"given":"Behnam","family":"Askarian","sequence":"first","affiliation":[{"name":"Department of Electrical and Computer Engineering, Texas Tech University, Lubbock, TX 79409, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Seung-Chul","family":"Yoo","sequence":"additional","affiliation":[{"name":"School of Communication &amp; Media, Ewha Womans University, Seoul 03760, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jo Woon","family":"Chong","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, Texas Tech University, Lubbock, TX 79409, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2019,7,27]]},"reference":[{"key":"ref_1","first-page":"358","article-title":"National hospital ambulatory medical care survey: 2007 emergency department summary","volume":"26","author":"Niska","year":"2010","journal-title":"Natl. 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