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首页> 外文期刊>International journal of reasoning-based intelligent systems >Deep convolutional neural network-based diabetic eye disease detection and classification using thermal images
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Deep convolutional neural network-based diabetic eye disease detection and classification using thermal images

机译：基于深度卷积神经网络的糖尿病眼病检测和分类使用热图像

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摘要

Infrared thermography which is non-contact and non-invasive technique is widely accepted as a medical diagnostic tool. Thermal images are processed for abnormality detection and quantification. It has been used in the diagnosis of dry eye, meibomian gland dysfunction, thyroid eye disease and glaucoma. Diabetic eye disease (DED) detection using thermal images is an absolutely new attempt. The early detection of the occurrence of DED can be very helpful for clinical treatment. In this paper, we are attempting towards finding an automatic way to classify DEDs in thermal images using a deep learning-based convolutional neural network (CNN) methodology. The sensitivity of 92.30%, specificity of 98.46%, and accuracy of 95.38% on testing dataset with reference to expert's ground truth results are obtained. The results attained evidently exhibit that the thermal imaging is promising modality and proposed deep learning method is capable for automatic diagnosis of diabetic eye disease classification.
机译：非接触和非侵入性技术的红外热成像被广泛接受为医疗诊断工具。处理热图像以进行异常检测和量化。它已被用于诊断干眼，睑板腺功能障碍，甲状腺眼病和青光眼。使用热图像的糖尿病眼病（DED）检测是绝对的尝试。早期检测到临床治疗的临床治疗非常有用。在本文中，我们正在尝试使用基于深度学习的卷积神经网络（CNN）方法来查找自动分类DEDS的热图像中的DED。获得的92.30％的95.38％，与基准测试数据集专家的地面实况结果检测灵敏度，98.46％，特异性和准确性。结果明显地表现出热成像是有前途的模态，并且提出的深度学习方法能够自动诊断糖尿病眼病分类。

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