Diabetic retinopathy is a leading cause of blindness in diabetic patients and early detection plays a crucial role in preventing vision loss. Traditional diagnostic methods are often time-consuming and prone to errors. The emergence of deep learning techniques has provided innovative solutions to improve diagnostic efficiency. However, single deep learning models frequently face issues related to extracting key features from complex retinal images. To handle this problem, we present an effective ensemble method for DR diagnosis comprising four main phases: image pre-processing, selection of backbone pre-trained models, feature enhancement, and optimization. Our methodology initiates with the pre-processing phase, where we apply CLAHE to enhance image contrast and Gamma correction is then used to adjust the brightness for better feature recognition. We then apply Discrete Wavelet Transform (DWT) for image fusion by combining multi-resolution details to create a richer dataset. Then, we selected three pre-trained models with the best performance named DenseNet169, MobileNetV1, and Xception for diverse feature extraction. To further improve feature extraction, an improved residual block is integrated into each model. Finally, the predictions from these base models are then aggregated using weighted ensemble approach, with the weights optimized by using Salp Swarm Algorithm (SSA).SSA intelligently explores the weight space and finds the optimal configuration of base architectures to maximize the performance of the ensemble model. The proposed model is evaluated on the multiclass Kaggle APTOS 2019 dataset and obtained 88.52% accuracy.

翻译：糖尿病视网膜病变是糖尿病患者致盲的主要原因，早期检测对预防视力丧失至关重要。传统诊断方法通常耗时且易出错。深度学习技术的出现为提高诊断效率提供了创新解决方案。然而，单一深度学习模型在处理复杂视网膜图像时，常面临关键特征提取不足的问题。为解决此问题，我们提出一种用于糖尿病视网膜病变诊断的有效集成方法，包含四个主要阶段：图像预处理、主干预训练模型选择、特征增强与优化。我们的方法始于预处理阶段，首先应用CLAHE增强图像对比度，随后采用伽马校正调整亮度以优化特征识别。接着应用离散小波变换（DWT）进行图像融合，通过结合多分辨率细节构建更丰富的数据集。随后，我们选取了三种性能最优的预训练模型——DenseNet169、MobileNetV1和Xception——以实现多样化特征提取。为进一步增强特征提取能力，每个模型均集成了改进的残差模块。最后，通过加权集成方法聚合这些基础模型的预测结果，其中权重采用樽海鞘群算法（SSA）进行优化。SSA智能探索权重空间，寻找基础架构的最优配置，以最大化集成模型的性能。所提模型在多类别Kaggle APTOS 2019数据集上进行评估，获得了88.52%的准确率。