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多类视网膜疾病OCT图像分类方法研究
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Abstract:
视网膜疾病是当前威胁人类视觉健康的重要问题之一，其早期诊断和干预对预防视力损害具有重要意义。光学相干断层扫描作为一种无创成像技术，在视网膜疾病诊断中发挥着关键作用。本文提出了一种基于改进DenseNet的深度学习模型，用于多类视网膜OCT图像的自动分类。在OCT-C8数据集上进行实验，结果表明改进后的模型在八类视网膜疾病的分类任务中表现优异，平均准确率达到99.41%。与现有其他方法相比，本文提出的模型展现出更优的分类性能。
Retinal diseases are one of the most important problems threatening human visual health, and their early diagnosis and intervention are of great significance in preventing visual impairment. Optical coherence tomography, as a non-invasive imaging technique, plays a key role in the diagnosis of retinal diseases. In this paper, a deep learning model based on improved DenseNet is proposed for the automatic classification of multi-class retinal OCT images. Experiments on the OCT-C8 dataset show that the improved model performs well in the classification task of eight retinal diseases with an average accuracy of 99.41%. Compared with other existing methods, the proposed model shows better classification performance.
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