The paper introduces a novel deep learning model called MAP Concordance Regressive Camargo’s Index-Based Deep Multilayer Perceptive Learning Classification (MAPCRCI-DMPLC) for early detection of diabetic retinopathy (DR). The model aims to improve the accuracy and reduce the time consumption in DR detection. The proposed model consists of an input layer, hidden layers, and an output layer. It employs a series of techniques, including MAP-estimated local region filtering for preprocessing, Camargo’s index-based region of interest (ROI) extraction, Concordance Correlative Regression for texture feature extraction, and color feature extraction. The swish activation function is used in the output layer to classify the images into different stages of DR. The model's performance is evaluated using a retinal image dataset, and it is compared with five state-of-the-art approaches in terms of peak signal-to-noise ratio (PSNR), disease detection accuracy (DDA), false-positive rate (FPR), and disease detection time (DDT). The results show that the MAPCRCI-DMPLC model outperforms the existing methods in terms of PSNR, DDA, FPR, and time complexity, demonstrating its effectiveness and efficiency in DR detection.The paper introduces a novel deep learning model called MAP Concordance Regressive Camargo’s Index-Based Deep Multilayer Perceptive Learning Classification (MAPCRCI-DMPLC) for early detection of diabetic retinopathy (DR). The model aims to improve the accuracy and reduce the time consumption in DR detection. The proposed model consists of an input layer, hidden layers, and an output layer. It employs a series of techniques, including MAP-estimated local region filtering for preprocessing, Camargo’s index-based region of interest (ROI) extraction, Concordance Correlative Regression for texture feature extraction, and color feature extraction. The swish activation function is used in the output layer to classify the images into different stages of DR. The model's performance is evaluated using a retinal image dataset, and it is compared with five state-of-the-art approaches in terms of peak signal-to-noise ratio (PSNR), disease detection accuracy (DDA), false-positive rate (FPR), and disease detection time (DDT). The results show that the MAPCRCI-DMPLC model outperforms the existing methods in terms of PSNR, DDA, FPR, and time complexity, demonstrating its effectiveness and efficiency in DR detection.