Image Classification Method in DR Image Based on Transfer Learning

Y. A. L. Alsabahi, Lei Fan, Xiaoyi Feng
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引用次数: 11

Abstract

Until now many cancer cases have been discovered in their early stages based on Computer Aided Diagnosis (CAD) system. There are many methods in the medical image processing field have been proposed to address this issue, and the result of these methods was deficient. Further, the application of AI in DR images is not widespread in hospitals. The classification process in the DR image is more difficult than other types of images. In this paper, we use transfer learning which is based on Inception V3 model to classify the DR images. We used the weight of Inception V3 model which was trained in the ImageNet dataset, and fine-tuning in our own dataset. Comparing to other proposed methods, our result had a higher accuracy.
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基于迁移学习的DR图像分类方法
目前,计算机辅助诊断(CAD)系统已经在早期发现了许多癌症病例。医学图像处理领域已经提出了许多方法来解决这一问题,但这些方法的结果都是不足的。此外,人工智能在DR图像中的应用在医院并不普遍。DR图像的分类过程比其他类型的图像更困难。本文采用基于Inception V3模型的迁移学习对DR图像进行分类。我们使用了在ImageNet数据集中训练的Inception V3模型的权重,并在我们自己的数据集中进行了微调。与其他提出的方法相比,我们的结果具有更高的精度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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