Detection Of Diabetic Retinopathy Diseases Research Articles

Transfer learning-driven ensemble model for detection of diabetic retinopathy disease.

In this study, we propose an ensemble model for the detection of diabetic retinopathy (DR) illness that is driven by transfer learning. Due to diabetes, the DR is a problem that affects the eyes. The retinal blood vessels in a person with high blood sugar deteriorate. The blood arteries may enlarge and leak as a result, or they may close and stop the flow of blood. If DR is not treated, it can become severe, damage vision, and eventually result in blindness. Medical experts study the colored fundus photos for this reason in order to manually diagnose disease, however this is a perilous technique. As a result, the condition was automatically identified utilizing retinal scans and a number of computer vision-based methods. A model is trained on one task or datasets employing the transfer learning (TL) technique, and then the pre-trained models or weights are applied to another task or dataset. Six deep learning (DL)-based convolutional neural network (CNN) models were trained in this study using huge datasets of reasonable photos, including DenseNet-169, VGG-19, ResNet101-V2, Mobilenet-V2, and Inception-V3. We also applied a data-preprocessing strategy to improve the accuracy and lower the training costs in order to improve the results. The experimental results demonstrate that the suggested model works better than existing approaches on the same dataset, with an accuracy of up to 98%, and detects the stage of DR.

Automatic detection of non-proliferative diabetic retinopathy in retinal fundus images using convolution neural network

Diabetic retinopathy (DR) is one of the complications of diabetes and a leading cause of blindness in the world. The tiny blood vessels inside the retina are damaged due to diabetes and result in various vision-related problems and it may lead to complete vision loss without early detection and treatment. Diabetic retinopathy may not cause any symptoms during its earlier stage of the disease and many physical tests such as visual acuity tests, pupil dilation, etc., are required to detect diabetic retinopathy disease. So, early detection of diabetic retinopathy disease is required to avoid vision loss. This work aims to automate the detection and grading of non-proliferative Diabetic Retinopathy from retinal fundus images using Convolution Neural Networks. The model was tested on two popular datasets such as MESSIDOR and IDRiD. Before applying the Convolution Neural Network (CNN) layers, the images were pre-processed and resolution was adjusted (256 × 256). The maximum accuracy achieved is 90.89% using MESSIDOR images. The research can be carried forward by applying various preprocessing techniques before putting them through different computational layers.

An effective image processing method for detection of diabetic retinopathy diseases from retinal fundus images

Diabetic retinopathy (i.e., DR), is an eye disorder caused by diabetes, diabetic retinopathy detection is an important task in retinal fundus images due the early detection and treatment can potentially reduce the risk of blindness. Retinal fundus images play an important role in diabetic retinopathy through disease diagnosis, disease recognition (i.e., by ophthalmologists), and treatment. The current state-of-the-art techniques are not satisfied with sensitivity and specificity. In fact, there are still other issues to be resolved in state-of-the-art techniques such as performances, accuracy, and easily identify the DR disease effectively. Therefore, this paper proposes an effective image processing method for detection of diabetic retinopathy diseases from retinal fundus images that will satisfy the performance metrics (i.e., sensitivity, specificity, accuracy). The proposed automatic screening system for diabetic retinopathy was conducted in several steps: Pre-processing, optic disc detection and removal, blood vessel segmentation and removal, elimination of fovea, feature extraction (i.e., Micro-aneurysm, retinal hemorrhage, and exudates), feature selection and classification. Finally, a software-based simulation using MATLAB was performed using DIARETDB1 dataset and the obtained results are validated by comparing with expert ophthalmologists. The results of the conducted experiments showed an efficient and effective in sensitivity, specificity and accuracy.

An effective image processing method for detection of diabetic retinopathy diseases from retinal fundus images

An effective image processing method for detection of diabetic retinopathy diseases from retinal fundus images