Microaneurysm (MA) is the earliest lesion of diabetic retinopathy (DR). Accurate detection of MA is helpful for the early diagnosis of DR. In this paper, an efficient approach is proposed to detect MA, based on feature-transfer network and local background suppression. In order to reduce noise, a feature-distance-based algorithm is proposed to suppress local background. The similarity matrix of feature distances is calculated to measure the difference between background noise and retinal objects. Moreover, a feature-transfer network is proposed to detect MAs with imbalanced data. For each training process, the optimized weights and bias are transferred to the next training, until the optimal network is generated. Experimental results demonstrate that the proposed approach can accurately detect subtle MAs surrounded by complex background. Furthermore, the sensitivity values on the public datasets are up to 98.3%, 100%, 99.3%, 100%, 96.5%, respectively. The proposed approach outperforms the state-of-the-arts, in terms of the competition performance measure score.

微动脉瘤（MA）是糖尿病性视网膜病变（DR）的最早病变。准确的MA检测有助于DR的早期诊断。本文提出了一种基于特征转移网络和局部背景抑制的有效检测MA的方法。为了减少噪声，提出了一种基于特征距离的算法来抑制局部背景。计算特征距离的相似度矩阵以测量背景噪声和视网膜物体之间的差异。此外，提出了一种特征转移网络来检测具有不平衡数据的MA。对于每个训练过程，将优化的权重和偏差转移到下一个训练，直到生成最佳网络。实验结果表明，该方法能够准确检测出复杂背景下的细微MA。此外，公开数据集上的敏感度值分别高达98.3％，100％，99.3％，100％，96.5％。就竞争绩效评估得分而言，所提出的方法优于最新技术。