基于视网膜眼底照片分类的高血压受试者神经网络检测

Yuki Sonetsuji, T. Isokawa, N. Kamiura, Hitoshi Tabuchi
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引用次数: 0

摘要

本文提出了一种利用卷积神经网络(convolutional neural networks,简称cnn)分类的视网膜眼底照片检测高血压受试者的方法。该方法采用Inception-v3模型作为CNN。提供给所提出的模型的数据是由视网膜眼底照片准备的。采用微调方案建立了判别模型。该模型指定了与受试者处于高血压状态相对应的照片。实验结果表明,该方法能获得较好的度量值。特别是对于小尺度的数据集,与之前提出的方法相比,该方法获得的精度值相对较高。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
On Neural-Network-Based Detection for Hypertensive Subjects Using Classification of Retinal Fundus Photographs
In this paper, a method of detecting hypertensive subjects is proposed, using retinal fundus photographs classified by convolutional neural networks (CNNs for short). The proposed method employs Inception-v3 model as a CNN. The data to be presented to the proposed model are prepared from retinal fundus photographs. The scheme of fine tuning is conducted to construct a discrimination model. The model specifies the photographs corresponding to the subjects being in hypertensive states. Experimental results establish that the proposed method can achieves favorable metric values. Especially, Accuracy value achieved by the proposed method is comparatively high for the small scale of dataset, compared with the previously proposed method.
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