Annu Int Conf IEEE Eng Med Biol Soc. 2022 Jul;2022:3862-3865. doi: 10.1109/EMBC48229.2022.9871989.
Diagnosis retinal abnormalities in Optical Coherence Tomography (OCT) images assist ophthalmologist in the early detection and treatment of patients. To do this, different Computer Aided Diagnosis (CAD) methods based on machine learning and deep learning algorithms have been proposed. In this paper, wavelet scattering network is used to identify normal retina and four pathologies namely, Central Serous Retinopathy (CSR), Macular Hole (MH), Age-related Macular Degeneration (AMD) and Diabetic Retinopathy (DR). Wavelet scattering network is a particular convolutional network which is formed from cascading wavelet transform with nonlinear modulus and averaging operators. This transform generates sparse, translation invariant and deformation stable representations of signals. Filters in the layers of this network are predefined wavelets and not need to be learned which causes decreasing the processing time and complexity. The extracted features are fed to a Principal Component Analysis (PCA) classifier. The results of this research show the accuracy of 97.4% and 100% in diagnosis abnormal retina and DR from normal ones, respectively. We also achieved the accuracy of 84.2% in classifying OCT images to five classes of normal, CSR, MH, AMD and DR which outperforms other state of the art methods with high computational complexity. Clinical Relevance- Clinically, the manually checking of each OCT B-scan by ophthalmologists is tedious and time consuming and may lead to an erroneous decision specially for multiclass problems. In this study, a low complexity CAD system for retinal OCT image classification based on wavelet scattering network is introduced which can be learned by a small number of data.
在光学相干断层扫描（OCT）图像中诊断视网膜异常有助于眼科医生早期发现和治疗患者。为此，已经提出了基于机器学习和深度学习算法的不同计算机辅助诊断（CAD）方法。在本文中，使用了基于小波散射网络来识别正常视网膜和四种病变，即中心性浆液性脉络膜视网膜病变（CSR）、黄斑裂孔（MH）、年龄相关性黄斑变性（AMD）和糖尿病性视网膜病变（DR）。小波散射网络是一种特殊的卷积网络，它由级联的小波变换与非线性模量和平均运算符组成。这种变换生成了稀疏、平移不变和变形稳定的信号表示。该网络层中的滤波器是预定义的小波，不需要学习，这降低了处理时间和复杂性。提取的特征被馈送到主成分分析（PCA）分类器。该研究的结果表明，在诊断异常视网膜和 DR 方面，其对正常视网膜的准确率分别为 97.4%和 100%。我们还在将 OCT 图像分类为正常、CSR、MH、AMD 和 DR 五个类别的准确率达到了 84.2%，优于其他具有高计算复杂度的最新方法。临床相关性-临床上，眼科医生手动检查每个 OCT B 扫描既繁琐又耗时，并且可能导致错误的决策，特别是对于多类问题。在这项研究中，引入了一种基于小波散射网络的低复杂度 CAD 系统，用于视网膜 OCT 图像分类，该系统可以通过少量数据进行学习。
