年龄相关性黄斑变性(AMD)和糖尿病性黄斑水肿(DME)是全球失明的重要原因。由于人口老龄化,这些疾病的患病率正在稳步上升。因此,早期诊断和预防是有效治疗的关键。黄斑变性OCT图像的分类是用于评估视网膜病变的广泛使用的方法。然而,OCT图像分类存在两个主要挑战:图像特征提取不完整和重要位置特征不突出。为了应对这些挑战,我们提出了一个名为MSA-Net的深度学习神经网络模型,它结合了我们提出的多尺度架构和空间注意力机制。我们的多尺度架构基于深度可分离卷积,这确保了从多个尺度进行全面的特征提取,同时最小化模型参数的增长。空间注意机制旨在突出图像中的重要位置特征,强调OCT图像中黄斑区域特征的表示。我们在NEH数据集和UCSD数据集上测试MSA-NET,执行三级(CNV,DURSEN,和正常)和四类(CNV,DURSEN,DME,和NORMAL)分类任务。在NEH数据集上,准确性,灵敏度,特异性为98.1%,97.9%,和98.0%,分别。在对UCSD数据集进行微调之后,准确性,灵敏度,特异性为96.7%,96.7%,98.9%,分别。实验结果表明,与以前的模型和最近著名的OCT分类模型相比,我们的模型具有出色的分类性能和泛化能力,将其确立为黄斑变性领域极具竞争性的情报分类方法。
Age-related macular degeneration (AMD) and diabetic macular edema (DME) are significant causes of blindness worldwide. The prevalence of these diseases is steadily increasing due to population aging. Therefore, early diagnosis and prevention are crucial for effective treatment. Classification of Macular Degeneration OCT Images is a widely used method for assessing retinal lesions. However, there are two main challenges in OCT image classification: incomplete image feature extraction and lack of prominence in important positional features. To address these challenges, we proposed a deep learning neural network model called MSA-Net, which incorporates our proposed multi-scale architecture and spatial attention mechanism. Our multi-scale architecture is based on depthwise separable convolution, which ensures comprehensive feature extraction from multiple scales while minimizing the growth of model parameters. The spatial attention mechanism is aim to highlight the important positional features in the images, which emphasizes the representation of macular region features in OCT images. We test MSA-NET on the NEH dataset and the UCSD dataset, performing three-class (CNV, DURSEN, and NORMAL) and four-class (CNV, DURSEN, DME, and NORMAL) classification tasks. On the NEH dataset, the accuracy, sensitivity, and specificity are 98.1%, 97.9%, and 98.0%, respectively. After fine-tuning on the UCSD dataset, the accuracy, sensitivity, and specificity are 96.7%, 96.7%, and 98.9%, respectively. Experimental results demonstrate the excellent classification performance and generalization ability of our model compared to previous models and recent well-known OCT classification models, establishing it as a highly competitive intelligence classification approach in the field of macular degeneration.