The prevalence of diabetic retinopathy (DR) has reached 34.6% worldwide and is a major cause of blindness among middle-aged diabetic patients. Regular DR screening using fundus photography helps detect its complications and prevent its progression to advanced levels. As manual screening is time-consuming and subjective, machine learning (ML) and deep learning (DL) have been employed to aid graders. However, the existing CNN-based methods use either pre-trained CNN models or a brute force approach to design new CNN models, which are not customized to the complexity of fundus images. To overcome this issue, we introduce an approach for custom-design of CNN models, whose architectures are adapted to the structural patterns of fundus images and better represent the DR-relevant features. It takes the leverage of k-medoid clustering, principal component analysis (PCA), and inter-class and intra-class variations to automatically determine the depth and width of a CNN model. The designed models are lightweight, adapted to the internal structures of fundus images, and encode the discriminative patterns of DR lesions. The technique is validated on a local dataset from King Saud University Medical City, Saudi Arabia, and two challenging benchmark datasets from Kaggle: EyePACS and APTOS2019. The custom-designed models outperform the famous pre-trained CNN models like ResNet152, Densnet121, and ResNeSt50 with a significant decrease in the number of parameters and compete well with the state-of-the-art CNN-based DR screening methods. The proposed approach is helpful for DR screening under diverse clinical settings and referring the patients who may need further assessment and treatment to expert ophthalmologists.
翻译:糖尿病视网膜病(DR)的流行程度已在全世界达到34.6%,是中年糖尿病患者失明的主要原因。定期使用Fundus摄影进行DR检测有助于发现其并发症,防止其发展到高级水平。由于人工筛选既费时又主观,机器学习(ML)和深学习(DL)已经用于帮助分级者。但是,以CNN为基础的现有方法使用预先培训的CNN模型或粗力方法来设计新的CNN模型,这些模型不是根据Fundus图像的复杂性定制的。为了克服这一问题,我们引入了CNN模型定制设计方法,其结构适应Fundus图像的结构模式,并更好地代表DRDR相关特征。它利用k-mood群集、主要组成部分分析(PCA)以及班级间和班级内部变异来自动确定CNN模型的深度和宽度。设计模型是轻量的,适应基于Fundus图像的内部结构,并记录了DRelvision的区别性模式。在沙特王室和王室数据库数据库数据库中,在沙特王室数据库数据库数据库数据库数据库数据库中用一个具有挑战性的数据模型进行验证。