Diabetic retinopathy detection via deep learning based dual features integrated classification model

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%.Conclusions: 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.