چکیده مقاله
In this article, we aimed to diagnose whether patients have bacterial pharyngitis ornonbacterial pharyngitis To achieve this, a dataset from 579 patients was collected, and at leastfour general practitioners diagnosed each sample Data augmentation methods were employedto increase the sample size, and various preprocessing techniques were applied to enhance thequality of images In this study, we utilized a Convolutional Neural Network CNN and twotransformers for binary classification The results demonstrate the capability of deep learningmodels to classify pharyngitis into bacterial and nonbacterial categories based on images takenby smartphone cameras with high accuracy
کلیدواژهها
نویسندگان
Negar Shojaei
Faculty of Intelligent System Engineering and Data Sciences, Persian Gulf University, Bushehr, Iran
Ali Behrouzi
Faculty of Intelligent System Engineering and Data Sciences, Persian Gulf University, Bushehr, Iran
Habib Rostami
Faculty of Intelligent System Engineering and Data Sciences, Persian Gulf University, Bushehr, Iran
Amir Sanati
Faculty of Intelligent System Engineering and Data Sciences, Persian Gulf University, Bushehr, Iran
Majid Alimohammadi
Department of of Medicine, Bushehr University of Medical Sciences, Bushehr, Iran
Jahanbakhsh Keyvani
Department of of Medicine, Shahrekord University of Medical Sciences, Chaharmahal va Bakhtiyari, Iran
شیوه ارجاع
Shojaei, Negar and Behrouzi, Ali and Rostami, Habib and Sanati, Amir and Alimohammadi, Majid and Keyvani, Jahanbakhsh,1403,Deep Learning Automated Differential Diagnosis of Pharyngitis using Smartphone Camera,1st International Biennial Conference of Artificial Intelligence and Data Science 2024,Bushehr
ارائهشده در
مجموعه مقالات اولین کنفرانس بین المللی دوسالانه هوش مصنوعی و علوم داده3 اردیبهشت 1403 · بوشهر