HFF-Net:用于糖尿病视网膜病变筛查和分级的混合卷积神经网络

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摘要

糖尿病视网膜病变(DR)是导致糖尿病患者视力丧失的主要原因,因此必须进行有效筛查和分级,以便及时干预。定期筛查大大增加了眼科医生的工作量,而准确分级--轻度、中度、重度和增殖期--对监测疾病进展至关重要。虽然计算机辅助诊断(CAD)系统可以减轻这一负担,但现有的基于卷积神经网络(CNN)的框架以线性前馈方式使用固定大小的核。由于相邻层之间的特征利用率有限,这种方法可能会导致初始阶段的信息丢失。为了解决这一局限性,我们在糖尿病视网膜病变筛查和分级(DRSG)框架内提出了分层特征融合卷积神经网络(HFF-Net)。该框架包括从眼底图像(FIs)中提取感兴趣区域的预处理、使用对比度受限自适应直方图均衡化(CLAHE)进行增强,以及用于类平衡和减轻过拟合的数据增强。HFF-Net 可提取多尺度特征,并在网络内多层次融合,利用swish 激活函数提高学习稳定性。我们在 DRSG 框架内对 HFF-Net 与几种最先进的 CNN 分类器进行了评估。实验结果表明,HFF-Net 的分级准确率达到了 73.77%,比排名第二的模型高出 3.51 个百分点(相对提高约 5%),并且仅使用 118 万个参数就达到了 98.70% 的筛选准确率。这些发现凸显了 HFF-Net 作为 CAD 系统中用于 DR 筛选和分级的高效工具的潜力。保留所有权利。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
HFF-Net: A hybrid convolutional neural network for diabetic retinopathy screening and grading
Diabetic Retinopathy (DR) is a leading cause of vision loss among diabetic patients, necessitating effective screening and grading for timely intervention. Regular screening significantly increases the workload of ophthalmologists, and accurate grading into stages—mild, moderate, severe, and proliferative—is crucial for monitoring disease progression. While Computer-Aided Diagnosis (CAD) systems can alleviate this burden, existing Convolutional Neural Network (CNN)-based frameworks use fixed-size kernels in a linear feed-forward manner. This approach can lead to information loss in the initial stages due to limited feature utilization across adjacent layers. To address this limitation, we propose a Hierarchical Features Fusion Convolutional Neural Network (HFF-Net) within a Diabetic Retinopathy Screening and Grading (DRSG) framework. The framework includes preprocessing to extract regions of interest from fundus images (FIs), enhancement using Contrast Limited Adaptive Histogram Equalization (CLAHE), and data augmentation for class balancing and overfitting mitigation. HFF-Net extracts multiscale features that fused at multiple levels within the network, utilizing the swish activation function for improved learning stability. We evaluated HFF-Net against several state-of-the-art CNN classifiers within the DRSG framework. Experimental results demonstrate that HFF-Net achieves a grading accuracy of 73.77 ​%, surpassing the second-best model by 3.51 percentage points (a relative improvement of approximately 5 ​%), and attains a screening accuracy of 98.70 ​% using only 1.18 million parameters. These findings highlight HFF-Net's potential as an efficient and effective tool in CAD systems for DR screening and grading.
©2017 Elsevier Inc. All rights reserved.
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