Diagnosis System For Diabetic Retinopathy Research Articles

A Benchmark for Studying Diabetic Retinopathy: Segmentation, Grading, and Transferability.

People with diabetes are at risk of developing an eye disease called diabetic retinopathy (DR). This disease occurs when high blood glucose levels cause damage to blood vessels in the retina. Computer-aided DR diagnosis has become a promising tool for the early detection and severity grading of DR, due to the great success of deep learning. However, most current DR diagnosis systems do not achieve satisfactory performance or interpretability for ophthalmologists, due to the lack of training data with consistent and fine-grained annotations. To address this problem, we construct a large fine-grained annotated DR dataset containing 2,842 images (FGADR). Specifically, this dataset has 1,842 images with pixel-level DR-related lesion annotations, and 1,000 images with image-level labels graded by six board-certified ophthalmologists with intra-rater consistency. The proposed dataset will enable extensive studies on DR diagnosis. Further, we establish three benchmark tasks for evaluation: 1. DR lesion segmentation; 2. DR grading by joint classification and segmentation; 3. Transfer learning for ocular multi-disease identification. Moreover, a novel inductive transfer learning method is introduced for the third task. Extensive experiments using different state-of-the-art methods are conducted on our FGADR dataset, which can serve as baselines for future research. Our dataset will be released in https://csyizhou.github.io/FGADR/.

An Optimal Deep Learning based Computer-aided Diagnosis System for Diabetic Retinopathy

Diabetic Retinopathy (DR) is a significant blinding disease that poses serious threat to human vision rapidly. Classification and severity grading of DR are difficult processes to accomplish. Traditionally, it depends on ophthalmoscopically-visible symptoms of growing severity, which is then ranked in a stepwise scale from no retinopathy to various levels of DR severity. This paper presents an ensemble of Orthogonal Learning Particle Swarm Optimization (OPSO) algorithm-based Convolutional Neural Network (CNN) Model EOPSO-CNN in order to perform DR detection and grading. The proposed EOPSO-CNN model involves three main processes such as preprocessing, feature extraction, and classification. The proposed model initially involves preprocessing stage which removes the presence of noise in the input image. Then, the watershed algorithm is applied to segment the preprocessed images. Followed by, feature extraction takes place by leveraging EOPSO-CNN model. Finally, the extracted feature vectors are provided to a Decision Tree (DT) classifier to classify the DR images. The study experiments were carried out using Messidor DR Dataset and the results showed an extraordinary performance by the proposed method over compared methods in a considerable way. The simulation outcome offered the maximum classification with accuracy, sensitivity, and specificity values being 98.47%, 96.43%, and 99.02% respectively.

Validation and application of an artificial intelligence robot assisted diagnosis system for diabetic retinopathy

Objective To evaluate the performance of an artificial intelligence (AI) assisted diagnosis system for diabetic retinopathy (DR) based on deep learning theory. Methods Diagnostic performance of a robot assisted diagnosis system called SongYue for DR was trained by using 25 297 retinal images tagged by fundus doctors from multiple hospitals in China.Four types of DR detection model consisting of abnormal DR, referable DR, severe non-proliferative and proliferative DR as well as proliferative DR according to fundus leisions identification were established.The ability of the system to distinguish DR was determined by using receiver operator characteristic (ROC) analysis, sensitivity and specificity of the system. Results SongYue system achieved an area under the ROC curve (AUC) of 0.920 for successfully distinguishing normal images from those DR with a sensitivity of 96.0% at a specificity of 87.9%.The AUC of SongYue for referable DR was 0.925, sensitivity was 90.4%, and specificity was 95.2%.For severe non-proliferative and proliferative DR, AUC was 0.845, sensitivity was 72.7%, and specificity was 96.2%.For proliferative DR, AUC was 0.855, sensitivity was 73.5%, and specificity was 97.3%. Conclusions SongYue robot assisted diagnosis system has high AUC, sensitivity and specificity for identifying DR, showing good clinical applicable benefits. Key words: Diabetic retinopathy; Artificial intelligence; Robot; Assisted diagnosis; Deep learning

Deep-learning-based automatic computer-aided diagnosis system for diabetic retinopathy.

The high-pace rise in advanced computing and imaging systems has given rise to a new research dimension called computer-aided diagnosis (CAD) system for various biomedical purposes. CAD-based diabetic retinopathy (DR) can be of paramount significance to enable early disease detection and diagnosis decision. Considering the robustness of deep neural networks (DNNs) to solve highly intricate classification problems, in this paper, AlexNet DNN, which functions on the basis of convolutional neural network (CNN), has been applied to enable an optimal DR CAD solution. The DR model applies a multilevel optimization measure that incorporates pre-processing, adaptive-learning-based Gaussian mixture model (GMM)-based concept region segmentation, connected component-analysis-based region of interest (ROI) localization, AlexNet DNN-based highly dimensional feature extraction, principle component analysis (PCA)- and linear discriminant analysis (LDA)-based feature selection, and support-vector-machine-based classification to ensure optimal five-class DR classification. The simulation results with standard KAGGLE fundus datasets reveal that the proposed AlexNet DNN-based DR exhibits a better performance with LDA feature selection, where it exhibits a DR classification accuracy of 97.93% with FC7 features, whereas with PCA, it shows 95.26% accuracy. Comparative analysis with spatial invariant feature transform (SIFT) technique (accuracy-94.40%) based DR feature extraction also confirms that AlexNet DNN-based DR outperforms SIFT-based DR.

Evolutionary Computing Enriched Computer-Aided Diagnosis System for Diabetic Retinopathy: A Survey.

Complications caused due to diabetes mellitus result in significant microvasculature that eventually causes diabetic retinopathy (DR) that keeps on increasing with time, and eventually causes complete vision loss. Identifying subtle variations in morphological changes in retinal blood vessels, optic disk, exudates, microaneurysms, hemorrhage, etc., is complicated and requires a robust computer-aided diagnosis (CAD) system so as to enable earlier and efficient DR diagnosis practices. In the majority of the existing CAD systems, functional enhancements have been realized time and again to ensure accurate and efficient diagnosis of DR. In this survey paper, a number of existing literature presenting DR CAD systems are discussed and analyzed. Both traditional and varoius evolutionary approaches, including genetic algorithm, particle swarm optimization, ant colony optimization, bee colony optimization, etc., based DR CAD have also been studied and their respective efficiencies have been discussed. Our survey revealed that evolutionary computing methods can play a vital role for optimizing DR-CAD functional components, such as proprocessing by enhancing filters coefficient, segmentation by enriching clustering, feature extraction, feature selection, and dimensional reduction, as well as classification.

REVIEW OF METHODS FOR DIABETIC RETINOPATHY DETECTION AND SEVERITY CLASSIFICATION

Diabetic Retinopathy is a serious vascular disorder that might lead to complete blindness. Therefore, the early detection and the treatment are necessary to prevent major vision loss. Though the Manual screening methods are available, they are time consuming and inefficient on a large image database of patients. Moreover, it demands skilled professionals for the diagnosis. Automatic Diabetic Retinopathy diagnosis systems can replace manual methods as they can significantly reduce the manual labor involved in the screening process. Screening conducted over a larger population can become efficient if the system can separate normal and abnormal cases, instead of the manual examination of all images. Therefore, Automatic Retinopathy detection systems have attracted large popularity in the recent times. Automatic retinopathy detection systems employ image processing and computer vision techniques to detect different anomalies associated with retinopathy. This paper reviews various methods of diabetic retinopathy detection and classification into different stages based on severity levels and also, various image databases used for the research purpose are discussed.

An automated decision-support system for non-proliferative diabetic retinopathy disease based on MAs and HAs detection

Diabetic retinopathy (DR) has become a serious threat in our society, which causes 45% of the legal blindness in diabetes patients. Early detection as well as the periodic screening of DR helps in reducing the progress of this disease and in preventing the subsequent loss of visual capability. This paper provides an automated diagnosis system for DR integrated with a user-friendly interface. The grading of the severity level of DR is based on detecting and analyzing the early clinical signs associated with the disease, such as microaneurysms (MAs) and hemorrhages (HAs). The system extracts some retinal features, such as optic disc, fovea, and retinal tissue for easier segmentation of dark spot lesions in the fundus images. That is followed by the classification of the correctly segmented spots into MAs and HAs. Based on the number and location of MAs and HAs, the system quantifies the severity level of DR. A database of 98 color images is used in order to evaluate the performance of the developed system. From the experimental results, it is found that the proposed system achieves 84.31% and 87.53% values in terms of sensitivity for the detection of MAs and HAs respectively. In terms of specificity, the system achieves 93.63% and 95.08% values for the detection of MAs and HAs respectively. Also, the proposed system achieves 68.98% and 74.91% values in terms of kappa coefficient for the detection of MAs and HAs respectively. Moreover, the system yields sensitivity and specificity values of 89.47% and 95.65% for the classification of DR versus normal.

Evaluation of a Computer-Aided Diagnosis System for Diabetic Retinopathy Screening on Public Data

To evaluate the performance of a comprehensive computer-aided diagnosis (CAD) system for diabetic retinopathy (DR) screening, using a publicly available database of retinal images, and to compare its performance with that of human experts. A previously developed, comprehensive DR CAD system was applied to 1200 digital color fundus photographs (nonmydriatic camera, single field) of 1200 eyes in the publicly available Messidor dataset (Methods to Evaluate Segmentation and Indexing Techniques in the Field of Retinal Ophthalmology (http://messidor.crihan.fr). The ability of the system to distinguish normal images from those with DR was determined by using receiver operator characteristic (ROC) analysis. Two experts also determined the presence of DR in each of the images. The system achieved an area under the ROC curve of 0.876 for successfully distinguishing normal images from those with DR with a sensitivity of 92.2% at a specificity of 50%. These compare favorably with the two experts, who achieved sensitivities of 94.5% and 91.2% at a specificity of 50%. This study shows, for the first time, the performance of a comprehensive DR screening system on an independent, publicly available dataset. The performance of the system on this dataset is comparable with that of human experts.