深度学习模型及其在渗出性咽炎诊断中的应用

IF 2.3 Q3 MEDICAL INFORMATICS
Healthcare Informatics Research Pub Date : 2024-01-01 Epub Date: 2024-01-31 DOI:10.4258/hir.2024.30.1.42
Seo Yi Chng, Paul Jie Wen Tern, Matthew Rui Xian Kan, Lionel Tim-Ee Cheng
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引用次数: 0

摘要

目的:远程医疗已在许多国家的医疗保健领域站稳脚跟。急性呼吸道感染是远程医疗会诊最常见的原因。咽喉检查对于诊断细菌性咽炎非常重要,但这对远程医疗会诊中的医生来说具有挑战性。一种解决方案是让患者将咽喉图像上传到网络应用程序。本研究旨在开发一种用于自动诊断渗出性咽炎的深度学习模型。此后,该模型将在线部署:我们在研究中使用了 343 张咽喉图像(139 张有渗出性咽炎,204 张没有咽炎)。我们使用 ImageDataGenerator 来扩充训练数据。使用 MobileNetV3、ResNet50 和 EfficientNetB0 的卷积神经网络模型对数据集进行训练,并对超参数进行调整:三个模型都训练成功;随着历时的增加,损失和训练损失减少,准确率和训练准确率增加。与 MobileNetV3(82.1%)和 ResNet50(88.1%)相比,EfficientNetB0 模型的准确率最高(95.5%)。EfficientNetB0 模型还获得了较高的精确度(1.00)、召回率(0.89)和 F1 分数(0.94):我们训练了一个基于 EfficientNetB0 的深度学习模型,它可以诊断渗出性咽炎。我们的模型能够达到 95.5% 的最高准确率,是之前所有使用机器学习诊断渗出性咽炎的研究中最高的。我们已将该模型部署到一个网络应用程序上,可用于辅助医生诊断渗出性咽炎。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Deep Learning Model and its Application for the Diagnosis of Exudative Pharyngitis.

Objectives: Telemedicine is firmly established in the healthcare landscape of many countries. Acute respiratory infections are the most common reason for telemedicine consultations. A throat examination is important for diagnosing bacterial pharyngitis, but this is challenging for doctors during a telemedicine consultation. A solution could be for patients to upload images of their throat to a web application. This study aimed to develop a deep learning model for the automated diagnosis of exudative pharyngitis. Thereafter, the model will be deployed online.

Methods: We used 343 throat images (139 with exudative pharyngitis and 204 without pharyngitis) in the study. ImageDataGenerator was used to augment the training data. The convolutional neural network models of MobileNetV3, ResNet50, and EfficientNetB0 were implemented to train the dataset, with hyperparameter tuning.

Results: All three models were trained successfully; with successive epochs, the loss and training loss decreased, and accuracy and training accuracy increased. The EfficientNetB0 model achieved the highest accuracy (95.5%), compared to MobileNetV3 (82.1%) and ResNet50 (88.1%). The EfficientNetB0 model also achieved high precision (1.00), recall (0.89) and F1-score (0.94).

Conclusions: We trained a deep learning model based on EfficientNetB0 that can diagnose exudative pharyngitis. Our model was able to achieve the highest accuracy, at 95.5%, out of all previous studies that used machine learning for the diagnosis of exudative pharyngitis. We have deployed the model on a web application that can be used to augment the doctor's diagnosis of exudative pharyngitis.

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来源期刊
Healthcare Informatics Research
Healthcare Informatics Research MEDICAL INFORMATICS-
CiteScore
4.90
自引率
6.90%
发文量
44
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