Toward automated severe pharyngitis detection with smartphone camera using deep learning networks

Abstract

Purpose Severe pharyngitis is frequently associated with inflammations caused by streptococcal pharyngitis, which can cause immune-mediated and post-infectious complications. The recent global pandemic of coronavirus disease (COVID-19) encourages the use of telemedicine for patients with respiratory symptoms. This study, therefore, purposes automated detection of severe pharyngitis using a deep learning framework with self-taken throat images. Methods A dataset composed of two classes of 131 throat images with pharyngitis and 208 normal throat images was collected. Before the training classifier, we constructed a cycle consistency generative adversarial network (CycleGAN) to augment the training dataset. The ResNet50, Inception-v3, and MobileNet-v2 architectures were trained with transfer learning and validated using a randomly selected test dataset. The performance of the models was evaluated based on the accuracy and area under the receiver operating characteristic curve (ROC-AUC). Results The CycleGAN-based synthetic images reflected the pragmatic characteristic features of pharyngitis. Using the synthetic throat images, the deep learning model demonstrated a significant improvement in the accuracy of the pharyngitis diagnosis. ResNet50 with GAN-based augmentation showed the best ROC-AUC of 0.988 for pharyngitis detection in the test dataset. In the 4-fold cross-validation using the ResNet50, the highest detection accuracy and ROC-AUC achieved were 95.3% and 0.992, respectively. Conclusion The deep learning model for smartphone-based pharyngitis screening allows fast identification of severe pharyngitis with a potential of the timely diagnosis of pharyngitis. In the recent pandemic of COVID-19, this framework will help patients with upper respiratory symptoms to improve convenience in diagnosis and reduce transmission.

Deep Learning Model and its Application for the Diagnosis of Exudative Pharyngitis

“…However, the AUC score of 0.975 in our study is slightly lower than the AUC score of 0.992 achieved by Yoo et al [ 13 ]. We also note that Yoo et al [ 13 ] and Askarian et al [ 14 ] did not deploy their models on a web or mobile application for clinical use.…”

“…The precision in this study was very high (1.00) and the recall was high (0.89). The two previous studies by Yoo et al [ 13 ] and Askarian et al [ 14 ] did not examine precision and recall; thus, we were not able to compare our models’ performance on these metrics with that of previous work. In our study where the precision was very high, at 1.00, the implication is that the model classified all non-pharyngitis patients correctly, thus avoiding the unnecessary use of antibiotics in patients who do not require them.…”

Detection of Tonsillopharyngitis with Grad-Cam and Optimization-Based Model Using Oropharyngeal Images

“…In addition, ambiguous images were eliminated by experts. While collecting the data set used in the study, images up to April 2020 were collected [9,10]. The related data set contains 131 images of the oropharynx with tonsillopharyngitis and 208 images of the normal oropharynx.…”

“…The accuracy value obtained by using 4-fold cross-validation in the ResNet50 model was 95.3%. The researchers stated that the proposed system could be used in smartphone-based applications [9].…”

Feasibility study to improve deep learning in OCT diagnosis of rare retinal diseases with few-shot classification

“…Burlina et al demonstrated the feasibility of using low-shot learning based on automated data augmentation to classify fundus photographs with rare conditions [ 18 ]. Several researchers have utilized generative models to enlarge training datasets in order to improve the detection accuracy of diseases using very small datasets [ 19 , 20 ]. Few-shot learning based on data augmentation has also been used to detect pathological chest images of patients with COVID-19 [ 21 ].…”