Devi T M, Karthikeyan P, Muthu Kumar B, Manikandakumar M
Department of Computer Science and Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai, 600062, India.
Department of Information Technology, Thiagarajar College of Engineering, Thiruparankundram, Tamil Nadu 625015, India.
Technol Health Care. 2025 Mar;33(2):1066-1080. doi: 10.1177/09287329241292939. Epub 2024 Dec 1.
BackgroundThe primary recognition of diabetic retinopathy (DR) is a pivotal requirement to prevent blindness and vision impairment. This deadly condition is identified by highly qualified professionals by examining colored retinal images.ObjectiveThe physical diagnostics for this condition was time-consuming and prone to fault. The development of computer-vision based intelligent systems has develop a main research area to effectually diagnosis the pathologies from an image.MethodsIn this research, a novel Deep learning based Dual Features Integrated classification (DD-FIC) framework is designed to detect the DR from a color retinal image. Initially, the fundus images are denoised by Wavelet integrated Retinex (WIR) algorithm to remove the noise artifacts which provide high contrast image. This DD-FIC model contains two phases of feature extraction module to evaluation of several retinal areas. Initially, global features of the fundus image are retrieved by the assist of attention fused efficient model, whereas the attention module dynamically highlights the important features. Afterwards, the segmented retinal vessels data is converted into features for learning the local features.ResultsFinally, the collective of features is processed into the Random Forest based feature selection model for the optimal prediction with five different classes using multi-class support vector machine (MCSVM). The efficacy of the proposed DD-FIC framework is estimated by Kaggle dataset with the detection accuracy of 98.6%. The proposed framework rises the accuracy of 1.54%, 3.65%, 13.79% and 6.28% for Multi-channel CNN, CNN, VGG NiN and Shallow CNN respectively.
背景
糖尿病视网膜病变（DR）的早期识别是预防失明和视力损害的关键要求。这种致命疾病由高素质专业人员通过检查彩色视网膜图像来识别。
目的
这种疾病的物理诊断耗时且容易出错。基于计算机视觉的智能系统的发展已成为从图像中有效诊断病变的一个主要研究领域。
方法
在本研究中，设计了一种基于深度学习的双特征集成分类（DD-FIC）框架，用于从彩色视网膜图像中检测DR。首先，通过小波集成视网膜算法（WIR）对眼底图像进行去噪，以去除噪声伪影，从而提供高对比度图像。这个DD-FIC模型包含两个特征提取模块阶段，用于评估几个视网膜区域。首先，借助注意力融合高效模型检索眼底图像的全局特征，而注意力模块动态突出重要特征。之后，将分割后的视网膜血管数据转换为特征，用于学习局部特征。
结果
最后，将这些特征集合输入基于随机森林的特征选择模型，使用多类支持向量机（MCSVM）对五个不同类别进行最优预测。通过Kaggle数据集评估所提出的DD-FIC框架的有效性，检测准确率为98.6%。所提出的框架分别将多通道卷积神经网络（Multi-channel CNN）、卷积神经网络（CNN）、VGG网络（VGG NiN）和浅卷积神经网络（Shallow CNN）的准确率提高了1.54%、3.65%、13.79%和6.28%。
