Background and objectives: Diabetic retinopathy is a type of diabetes that causes vascular changes that can lead to blindness. The ravages of this disease cannot be reversed, so early detection is essential. This work presents an automated method for early detection of this disease using fundus colored images.

Methods: A bio-inspired approach is proposed on synaptic metaplasticity in convolutional neural networks. This biological phenomenon is known to directly interfere in both learning and memory by reinforcing less common occurrences during the learning process. Synaptic metaplasticity has been included in the backpropagation stage of a convolution operation for every convolutional layer.

Results: The proposed method has been evaluated by using a public small diabetic retinopathy dataset from Kaggle with four award-winning convolutional neural network architectures. Results show that convolutional neural network architectures including synaptic metaplasticity improve both learning rate and accuracy. Furthermore, obtained results outperform other methods in current literature, even using smaller datasets for training. Best results have been obtained for the InceptionV3 architecture with synaptic metaplasticity with a 95.56% accuracy, 94.24% F1-score, 98.9% precision and 90% recall, using 3662 images for training.

Conclusions: Convolutional neural networks with synaptic metaplasticity are suitable for early detection of diabetic retinopathy due to their fast convergence rate, training simplicity and high performance.

背景与目的：糖尿病性视网膜病是一种糖尿病，会引起血管变化并导致失明。这种疾病的肆虐无法逆转，因此及早发现是必不可少的。这项工作提出了一种使用眼底彩色图像早期检测这种疾病的自动化方法。

方法：在卷积神经网络中提出了一种生物启发性的方法来解决突触的可塑性。众所周知，这种生物现象通过在学习过程中加强不太常见的现象而直接干扰学习和记忆。对于每个卷积层，在卷积操作的反向传播阶段都包含了突触的可塑性。

结果：通过使用来自Kaggle的公开的小型糖尿病视网膜病变数据集和四个屡获殊荣的卷积神经网络体系结构，对所提出的方法进行了评估。结果表明，包括突触可塑性的卷积神经网络体系结构可提高学习速度和准确性。此外，即使使用较小的数据集进行训练，所获得的结果也优于当前文献中的其他方法。使用3662张图像进行训练，InceptionV3具有突触可塑性的构架获得了最佳结果，其准确度为95.56％，F1评分为94.24％，准确度为98.9％和召回率为90％。

结论：具有突触可塑性的卷积神经网络具有收敛速度快，训练简单和性能高等特点，因此适合于糖尿病视网膜病变的早期检测。