Automatic screening and classification of diabetic retinopathy and maculopathy using fuzzy image processing

Digital retinal imaging is a challenging screening method for which effective, robust and cost-effective approaches are still to be developed. Regular screening for diabetic retinopathy and diabetic maculopathy diseases is necessary in order to identify the group at risk of visual impairment. This paper presents a novel automatic detection of diabetic retinopathy and maculopathy in eye fundus images by employing fuzzy image processing techniques. The paper first introduces the existing systems for diabetic retinopathy screening, with an emphasis on the maculopathy detection methods. The proposed medical decision support system consists of four parts, namely: image acquisition, image preprocessing including four retinal structures localisation, feature extraction and the classification of diabetic retinopathy and maculopathy. A combination of fuzzy image processing techniques, the Circular Hough Transform and several feature extraction methods are implemented in the proposed system. The paper also presents a novel technique for the macula region localisation in order to detect the maculopathy. In addition to the proposed detection system, the paper highlights a novel online dataset and it presents the dataset collection, the expert diagnosis process and the advantages of our online database compared to other public eye fundus image databases for diabetic retinopathy purposes

Automatic Diagnosis of Diabetic Retinopathy Using Morphological Operations

Diabetic retinopathy is diabetic eye disease or a sight threatening complication (one of the major cause of blindness) for the person suffering from diabetes which causes progressive loss to the retina, in which retina of the eye is affected because the capillaries of the retina are damaged. Diabetic Retinopathy is unpredictable at early stage, it is only predictable in advanced stage when diabetic patient suffers from loss of vision due to leakage of lipid, blood vessels bursts and there is formation of new fragile blood vessels which blocks the blood supply to retina. Diabetic Retinopathy include Microaneurysm, hemorrhage and exudates. However, early detection and treatment is most important that can reduce the chances of occurrences of blindness about 95%. To analyze Microaneurysm and hemorrhage as early stages of DR is a challenging task for Ophthalmologists to prevent vision loss. Automatic analysis of Diabetic Retinopathy helps in preventing vision loss. Our proposed method is based on automatic detection of hemorrhage using colorful fundus images. In proposed work we have used supervised learning to classify the data as hemorrhage and without hemorrhage with SVM classifier. To find hemorrhage and its severity, we have extracted statistical features (including standard deviation, energy, entropy and contrast of an image), used classification approach and then segmentation methods. After feature detection, Morphological Operations are applied to detect blood vessels and hemorrhage detection with help of segmentation technique. Here the threshold optimization, Grey Wolf Optimization (GWO) techniques are used in our proposed work for getting maximum accuracy, sensitivity and specificity performance metrics

UNRAVELLING DIABETIC RETINOPATHY THROUGH IMAGE PROCESSING, NEURAL NETWORKS AND FUZZY LOGIC – A REVIEW

One of the main causes of blindness is diabetic retinopathy (DR) and it may affect people of any ages. In these days, both young and old ages are affected by diabetes, and the di abetes is the main cause of DR. Hence, it is necessary to have an automated system with good accuracy and less computation time to diagnose and treat DR, and the automated system can simplify the work of ophthalmologists. The objective is to present an overview of various works recently in detecting and segmenting the various lesions of DR. Papers were categorized based on the diagnosing tools and the methods used for detecting early and advanced stage lesions. The early lesions of DR are microaneurysms, hemorrhages, exudates, and cotton wool spots and in the advanced stage, new and fragile blood vessels can be grown. Results have been evaluated in terms of sensitivity, specificity, accuracy and receiver operating characteristic curve. This paper analyzed the various steps and different algorithms used recently for the detection and classification of DR lesions. A comparison of performances has been made in terms of sensitivity, specificity, area under the curve, and accuracy. Suggestions, future workand the area to be improved were also discussed.Keywords: Diabetic retinopathy, Image processing, Morphological operations, Neural network, Fuzzy logic.Â

MULTISTAGE CLASSIFICATION OF DIABETIC RETINOPATHY USING FUZZY-NEURAL NETWORK CLASSIFIER

Diabetic Retinopathy (DR) is complicated disorder in human retina which is affected due to an increasing amount of insulin in blood that results in vision impairment. Early detection of DR is used to support the patients to prevent blindness and to be aware of this disease. This paper proposes a novel technique for detecting DR using hybrid classifiers. It includes pre-processing of the image, segmentation of region of interest, feature extraction and classification. Retinal structures like microaneurysms, exudates, hemorrhages and blood vessels are segmented. Classification is performed with integration of Fuzzy logical System and Neural Network (NN) which improves the accuracy of classification. Experimentation is carried out with the MESSIDOR data set. Results are compared against various performance metrics like accuracy, sensitivity and specificity. An accuracy close to 100 percent and low average error rate of 0.012 are obtained using the proposed method. The results obtained are encouraging

Automatic Screening and Classification of Diabetic Retinopathy Eye Fundus Image

Diabetic Retinopathy (DR) is a disorder of the retinal vasculature. It develops to some degree in nearly all patients with long-standing diabetes mellitus and can result in blindness. Screening of DR is essential for both early detection and early treatment. This thesis aims to investigate automatic methods for diabetic retinopathy detection and subsequently develop an effective system for the detection and screening of diabetic retinopathy. The presented diabetic retinopathy research involves three development stages. Firstly, the thesis presents the development of a preliminary classification and screening system for diabetic retinopathy using eye fundus images. The research will then focus on the detection of the earliest signs of diabetic retinopathy, which are the microaneurysms. The detection of microaneurysms at an early stage is vital and is the first step in preventing diabetic retinopathy. Finally, the thesis will present decision support systems for the detection of diabetic retinopathy and maculopathy in eye fundus images. The detection of maculopathy, which are yellow lesions near the macula, is essential as it will eventually cause the loss of vision if the affected macula is not treated in time. An accurate retinal screening, therefore, is required to assist the retinal screeners to classify the retinal images effectively. Highly efficient and accurate image processing techniques must thus be used in order to produce an effective screening of diabetic retinopathy. In addition to the proposed diabetic retinopathy detection systems, this thesis will present a new dataset, and will highlight the dataset collection, the expert diagnosis process and the advantages of the new dataset, compared to other public eye fundus images datasets available. The new dataset will be useful to researchers and practitioners working in the retinal imaging area and would widely encourage comparative studies in the field of diabetic retinopathy research. It is envisaged that the proposed decision support system for clinical screening would greatly contribute to and assist the management and the detection of diabetic retinopathy. It is also hoped that the developed automatic detection techniques will assist clinicians to diagnose diabetic retinopathy at an early stage

Diabetic Retinopathy Identification Using Parallel Convolutional Neural Network Based Feature Extractor and ELM Classifier

Diabetic retinopathy (DR) is an incurable retinal condition caused by excessive blood sugar that, if left untreated, can result in even blindness. A novel automated technique for DR detection has been proposed in this paper. To accentuate the lesions, the fundus images (FIs) were preprocessed using Contrast Limited Adaptive Histogram Equalization (CLAHE). A parallel convolutional neural network (PCNN) was employed for feature extraction and then the extreme learning machine (ELM) technique was utilized for the DR classification. In comparison to the similar CNN structure, the PCNN design uses fewer parameters and layers, which minimizes the time required to extract distinctive features. The effectiveness of the technique was evaluated on two datasets (Kaggle DR 2015 competition (Dataset 1; 34,984 FIs) and APTOS 2019 (3,662 FIs)), and the results are promising. For the two datasets mentioned, the proposed technique attained accuracies of 91.78 % and 97.27 % respectively. However, one of the study's subsidiary discoveries was that the proposed framework demonstrated stability for both larger and smaller datasets, as well as for balanced and imbalanced datasets. Furthermore, in terms of classifier performance metrics, model parameters and layers, and prediction time, the suggested approach outscored existing state-of-the-art models, which would add significant benefit for the medical practitioners in accurately identifying the DR

Preval?ncia de maculopatia diab?tica em primeira consulta, diagnosticada por tomografia de coer?ncia ?ptica em uma cl?nica de oftalmologia do norte do Estado

Introdu??o : A preval?ncia de pessoas vivendo com diabetes em 2018 ? de 425 milh?es. A retinopatia diab?tica (RD) ? uma das complica??es mais comuns do diabetes, e o edema macular diab?tico (EMD) ? a principal causa de cegueira no mundo. O exame padr?o ouro para o diagn?stico do EMD ? tomografia de coer?ncia ?ptica (OCT). M?todos: Foram selecionados pacientes com edema macular observado em OCT realizadas no ano de 2017 em uma cl?nica de oftalmologia. Ap?s a sele??o, foram analisados os prontu?rios desses pacientes e coletadas as informa??es sobre a presen?a de diabetes, estado de tratamento e preval?ncia de maculopatia diab?tica evidenciadas nas tomografias de coer?ncia ?ptica (OCT) na primeira consulta dos pacientes na referida cl?nica. Resultados: Foram analisadas 193 OCT, destas 69 (35,75%) apresentavam aumento da espessura foveal em rela??o ? normalidade, 16 (23,19%) apresentavam EMD. Desses 16 com EMD, 2 o apresentaram na na primeira consulta. Assim a preval?ncia de EMD na primeira consulta foi de 2,90%. Discuss?o: Esse estudo foi realizado em uma cl?nica de oftalmologia particular, utilizando o exame padr?o ouro para o diagn?stico de EMD, por essa raz?o pode diferir dos dados estatisticamente encontrados para pacientes diab?ticos assistidos pelo Sistema ?nico de Sa?de. Conclus?o: O diabetes pode afetar de maneira significativa a regi?o macular causando defici?ncia visual e o controle adequado da glicemia ? a melhor estrat?gia para controle da RD. Esse estudo teve como limita??o o tamanho da amostra, considerado pequeno em raz?o da grande frequ?ncia da patologia abordada