Department of Information and Telecommunication Engineering, Gangeung-Wonju National University, Wonju, Korea.
Department of Electronic Engineering, Gachon University, Seongnam, Korea.
Technol Health Care. 2023;31(S1):423-428. doi: 10.3233/THC-236036.
Eye exam diagnosis is one of the early detection methods for eye diseases. However, such a method is dependent on expensive and unpredictable optical equipment.
The eye exam can be re-emerged through an optometric lens attached to a smartphone and come to read the diseases automatically. Therefore, this study aims to provide a stable and predictable model with a given dataset representing the target group domain and develop a new method to identify eye disease with accurate and stable performance.
The ResNet-18 models pre-trained on ImageNet data composed of 1,000 everyday objects were employed to learn the dataset's features and validate the test dataset separated from the training dataset.
A proposed model showed high training and validation accuracy values of 99.1% and 96.9%, respectively.
The designed model could produce a robust and stable eye disease discrimination performance.
眼科检查诊断是眼部疾病早期检测的方法之一。然而，这种方法依赖于昂贵且不可预测的光学设备。
通过将验光镜片附加到智能手机上来重新实现眼科检查，并实现自动读取疾病。因此，本研究旨在提供一种稳定且可预测的模型，该模型使用代表目标群体域的给定数据集，并开发一种新的方法来识别具有准确和稳定性能的眼部疾病。
使用在包含 1000 个日常物品的 ImageNet 数据上预训练的 ResNet-18 模型来学习数据集的特征，并验证从训练数据集中分离出的测试数据集。
提出的模型表现出 99.1%和 96.9%的高训练和验证精度值。
设计的模型可以产生稳健且稳定的眼部疾病鉴别性能。
