An Effective Approach Based on Improved Convolutionary Neural Network Classifier for the Detection of Diabetic Retinopathy

2021 ◽  
Vol 11 (12) ◽  
pp. 3082-3089
Author(s):  
B. Sakthi Karthi Durai ◽  
J. Benadict Raja
Keyword(s):  
Diabetic Retinopathy ◽  
Vision Loss ◽  
Early Stage ◽  
Principal Component ◽  
Gabor Filtering ◽  
Processing Scheme ◽  
Successful Approach ◽  
Image Pixels ◽  
Watershed Method ◽  
In The Beginning

In diabetic individuals, diabetic retinopathy (DR) causes blindness. Therefore, detecting diabetic retinopathy at an early stage decreases vision loss. An successful approach for diabetic retinopathy prediction is discussed in this article. In the beginning, the input pictures of human retinal fundus images are preprocessed using histogram equalisation followed by Gabor filtering to reduce noise for enhancement. Then, using the Watershed method, segmentation is performed, and the features are retrieved through feature extraction. The best optimum features are selected using PCA (principal component analysis) approach. The morphological based post processing scheme was employed to further enhance the quality of selected features. At last, the classification approach is carried with the utilization of Google NET CNN classifier to classify/predict the retinal image as normal, abnormal, and severe. Google NET CNN has been developed with limited preprocessing step to distinguish visual features directly from image pixels. The findings are then evaluated and the efficacy of the new method is contrasted with other current methods. The quantitative findings were evaluated for Accuracy, precision, reliability, positive predictive levels and false predictive levels in parameters and were seen to deliver better results than current techniques.

scholarly journals Classification among Microaneurysms, Exudates, and Lesion free Retinal Regions in the Eye Images using Transfer Learned CNNs

2019 ◽  
Vol 9 (2) ◽  
pp. 5508-5512
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Diabetic Retinopathy ◽  
Glucose Level ◽  
Vision Loss ◽  
Early Stage ◽  
Training Data ◽  
Fundus Images ◽  
Diabetic Mellitus ◽  
Start Process

When pancreas fails to secrete sufficient insulin in the human body, the glucose level in blood either becomes too high or too low. This fluctuation in glucose level affects different body organs such as kidney, brain, and eye. When the complications start appearing in the eyes due to Diabetic Mellitus (DM), it is called Diabetic Retinopathy (DR). DR can be categorized in several classes based on the severity, it can be Microaneurysms (ME), Haemorrhages (HE), Hard and Soft Exudates (EX and SE). DR is a slow start process that starts with very mild symptoms, becomes moderate with the time and results in complete vision loss, if not detected on time. Early-stage detection may greatly bolster in vision loss. However, it is impassable to detect the symptoms of DR with naked eyes. Ophthalmologist harbor to the several approaches and algorithm which makes use of different Machine Learning (ML) methods and classifiers to overcome this disease. The burgeoning insistence of Convolutional Neural Network (CNN) and their advancement in extracting features from different fundus images captivate several researchers to strive on it. Transfer Learning (TL) techniques help to use pre-trained CNN on a dataset that has finite training data, especially that in under developing countries. In this work, we propose several CNN architecture along with distinct classifiers which segregate the different lesions (ME and EX) in DR images with very eye-catching accuracies.

scholarly journals Early Detection of Diabetic Retinopathy Using PCA-Firefly Based Deep Learning Model

Electronics ◽  
2020 ◽  
Vol 9 (2) ◽  
pp. 274 ◽  
Author(s):  
Thippa Reddy Gadekallu ◽  
Neelu Khare ◽  
Sweta Bhattacharya ◽  
Saurabh Singh ◽  
Praveen Kumar Reddy Maddikunta ◽  
...  
Keyword(s):  
Machine Learning ◽  
Diabetic Retinopathy ◽  
Deep Learning ◽  
Early Detection ◽  
Dimensionality Reduction ◽  
Vision Loss ◽  
Principal Component ◽  
Screening Methods ◽  
Learning Approaches ◽  
Time Period

Diabetic Retinopathy is a major cause of vision loss and blindness affecting millions of people across the globe. Although there are established screening methods - fluorescein angiography and optical coherence tomography for detection of the disease but in majority of the cases, the patients remain ignorant and fail to undertake such tests at an appropriate time. The early detection of the disease plays an extremely important role in preventing vision loss which is the consequence of diabetes mellitus remaining untreated among patients for a prolonged time period. Various machine learning and deep learning approaches have been implemented on diabetic retinopathy dataset for classification and prediction of the disease but majority of them have neglected the aspect of data pre-processing and dimensionality reduction, leading to biased results. The dataset used in the present study is a diabetes retinopathy dataset collected from the UCI machine learning repository. At its inceptions, the raw dataset is normalized using the Standardscalar technique and then Principal Component Analysis (PCA) is used to extract the most significant features in the dataset. Further, Firefly algorithm is implemented for dimensionality reduction. This reduced dataset is fed into a Deep Neural Network Model for classification. The results generated from the model is evaluated against the prevalent machine learning models and the results justify the superiority of the proposed model in terms of Accuracy, Precision, Recall, Sensitivity and Specificity.

scholarly journals Diabetic retinopathy: treatment and prevention

2007 ◽  
Vol 4 (3_suppl) ◽  
pp. S9-S11 ◽  
Author(s):  
Paul M Dodson
Keyword(s):  
Diabetic Retinopathy ◽  
Standard Therapy ◽  
Vision Loss ◽  
Early Stage ◽  
Developed Countries ◽  
Relative Reduction ◽  
Treatment And Prevention ◽  
Working Age ◽  
Capillary Occlusion

Diabetic eye disease is the major cause of blindness and vision loss among working-age people in developed countries. Microangiopathy and capillary occlusion underlie the pathogenesis of disease. While laser treatment is regarded as the standard therapy, intensive medical management of glycaemia and hypertension is also a priority in order to reduce the risk of diabetic retinopathy. Recent data have prompted a re-evaluation of the role of lipid-modifying therapy in reducing diabetic retinopathy. The Fenofibrate Intervention for Event Lowering in Diabetes (FIELD) study demonstrated a significant 30% relative reduction in the need for first retinal laser therapy in patients with (predominantly early-stage) type 2 diabetes treated with fenofibrate 200 mg daily, from 5.2% with placebo to 3.6% with fenofibrate, p=0.0003. The benefit of fenofibrate was evident within the first year of treatment. These promising data justify further evaluation of the mechanism and role of fenofibrate, in addition to standard therapy, in the management of diabetic retinopathy.

scholarly journals Deep Learning Approach for Stages of Severity Classification in Diabetic Retinopathy Using Color Fundus Retinal Images

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Silky Goel ◽  
Siddharth Gupta ◽  
Avnish Panwar ◽  
Sunil Kumar ◽  
Madhushi Verma ◽  
...  
Keyword(s):  
Diabetic Retinopathy ◽  
Deep Learning ◽  
Vision Loss ◽  
Early Stage ◽  
Machine Learning Algorithms ◽  
Fundus Photography ◽  
Huge Amount ◽  
Early Screening ◽  
Image Metadata ◽  
Very High

Diabetes is a very fast-growing disease in India, with currently more than 72 million patients. Prolonged diabetes (about almost 20 years) can cause serious loss to the tiny blood vessels and neurons in the patient eyes, called diabetic retinopathy (DR). This first causes occlusion and then rapid vision loss. The symptoms of the disease are not very conspicuous in its early stage. The disease is featured by the formation of bloated structures in the retinal area called microaneurysms. Because of negligence, the condition of the eye worsens into the generation of more severe blots and damage to retinal vessels causing complete loss of vision. Early screening and monitoring of DR can reduce the risk of vision loss in patients with high possibilities. But the diabetic retinopathy detection and its classification by a human, is a challenging and error-prone task, because of the complexity of the image captured by color fundus photography. Machine learning algorithms armed with some feature extraction techniques have been employed earlier to detect and classify the levels of DR. However, these techniques provide below-par accuracy. Now, with the advent of deep learning (DL) techniques in computer vision, it has become possible to achieve very high levels of accuracy. DL models are an abstraction of the human brain coupled with the eyes. To create a model from scratch and train it is a cumbersome task requiring a huge amount of images. This deficiency of the DL techniques can be patched up by employing another technique to a task called transfer learning. In this, a DL model is trained on image metadata, and to learn features it used hundreds of classes from the DR fundus images. This enables professionals to create models capable of classifying unseen images into a proper grade or level with acceptable accuracy. This paper proposed a DL model coupled with different classifiers to classify the fundus image into its correct class of severity. We have trained the model on IDRD images and it has proven to show very high accuracy.

scholarly journals Severity Classification of Diabetic Retinopathy Using an Ensemble Learning Algorithm through Analyzing Retinal Images

Symmetry ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 670
Author(s):  
Niloy Sikder ◽  
Mehedi Masud ◽  
Anupam Kumar Bairagi ◽  
Abu Shamim Mohammad Arif ◽  
Abdullah-Al Nahid ◽  
...  
Keyword(s):  
Diabetic Retinopathy ◽  
Ensemble Learning ◽  
Vision Loss ◽  
Learning Algorithm ◽  
Early Stage ◽  
Texture Features ◽  
Asia Pacific ◽  
Health Concern ◽  
Feature Selection Technique ◽  
Margin Of Error

Diabetic Retinopathy (DR) refers to the damages endured by the retina as an effect of diabetes. DR has become a severe health concern worldwide, as the number of diabetes patients is soaring uncountably. Periodic eye examination allows doctors to detect DR in patients at an early stage to initiate proper treatments. Advancements in artificial intelligence and camera technology have allowed us to automate the diagnosis of DR, which can benefit millions of patients indeed. This paper inscribes a novel method for DR diagnosis based on the gray-level intensity and texture features extracted from fundus images using a decision tree-based ensemble learning technique. This study primarily works with the Asia Pacific Tele-Ophthalmology Society 2019 Blindness Detection (APTOS 2019 BD) dataset. We undertook several steps to curate its contents to make them more suitable for machine learning applications. Our approach incorporates several image processing techniques, two feature extraction techniques, and one feature selection technique, which results in a classification accuracy of 94.20% (margin of error: ±0.32%) and an F-measure of 93.51% (margin of error: ±0.5%). Several other parameters regarding the proposed method’s performance have been presented to manifest its robustness and reliability. Details on each employed technique have been included to make the provided results reproducible. This method can be a valuable tool for mass retinal screening to detect DR, thus drastically reducing the rate of vision loss attributed to it.

scholarly journals Pemafibrate Protects Against Retinal Dysfunction in a Murine Model of Diabetic Retinopathy

2020 ◽  
Vol 21 (17) ◽  
pp. 6243 ◽  
Author(s):  
Yohei Tomita ◽  
Deokho Lee ◽  
Yukihiro Miwa ◽  
Xiaoyan Jiang ◽  
Masayuki Ohta ◽  
...  
Keyword(s):  
Diabetic Retinopathy ◽  
Blood Glucose ◽  
Vision Loss ◽  
Early Stage ◽  
Neural Function ◽  
Peroxisome Proliferator ◽  
Diabetic Mice ◽  
Peroxisome Proliferator Activated Receptor ◽  
Glucose Levels ◽  
Adult Mice

Diabetic retinopathy (DR) is one of the leading causes of blindness globally. Retinal neuronal abnormalities occur in the early stage in DR. Therefore, maintaining retinal neuronal activity in DR may prevent vision loss. Previously, pemafibrate, a novel selective peroxisome proliferator-activated receptor alpha modulator, was suggested as a promising drug in hypertriglyceridemia. However, the role of pemafibrate remains obscure in DR. Therefore, we aimed to unravel systemic and retinal changes by pemafibrate in diabetes. Adult mice were intraperitoneally injected with streptozotocin (STZ) to induce diabetes. After STZ injection, diet supplemented with pemafibrate was given to STZ-induced diabetic mice for 12 weeks. During the experiment period, body weight and blood glucose levels were examined. Electroretinography was performed to check the retinal neural function. After sacrifice, the retina, liver, and blood samples were subjected to molecular analyses. We found pemafibrate mildly improved blood glucose level as well as lipid metabolism, boosted liver function, increased serum fibroblast growth factor21 level, restored retinal functional deficits, and increased retinal synaptophysin protein expression in STZ-induced diabetic mice. Our present data suggest a promising pemafibrate therapy for the prevention of early DR by improving systemic metabolism and protecting retinal function.

scholarly journals The Change of Retinal Microvascular, Microstructure and Expression of IL-6, CD18, ICAM, TNF-α and VEGF in The Early Stage of Rat Diabetic Retinopathy

2021 ◽  
Author(s):  
Ang Xiao ◽  
HuiFeng Zhong ◽  
Lei Xiong ◽  
Lin Yang ◽  
YunFang Xu ◽  
...  
Keyword(s):  
Diabetic Retinopathy ◽  
Vision Loss ◽  
Early Stage ◽  
Imaging Techniques ◽  
Periodic Acid ◽  
Normal Group ◽  
High Expression ◽  
Vegf Mrna ◽  
Diabetes Group ◽  
Tnf Α

Abstract Background: Diabetic retinopathy (DR) is a leading cause of vision loss and blindness. The purpose of this project is to deeply observe the change of retinal microvascular, microstructure and expression of IL-6, CD18, ICAM, TNF-α and VEGF at the early stage of DR in rats with streptozotocin-induced diabetes mellitus (DM). Methods: The fluorescein fundus angiography was used to examine fundus of living organisms, retinas were obtained for hematoxylin and eosin staining, periodic acid-Schiff staining, fluorescence imaging techniques and quantitative real-time PCR, while vitreous humors were isolated for vascular endothelial growth factor (VEGF)-A ELISA in diabetes group (n=25) and normal group (n=25) at 8th day, 4th week, 6th week, 8th week and 10th week after the onset of DM. Results: In this study, we observed not only the decrease of RGCs and the increase of E/P ratio, acellular capillaries and type IV collagen-positive strands began to occur on 8th day after induction, but the vascular permeability and neovascularization buds began to happen in diabetes group in 8th week, while the expression of VEGF-A, VEGF mRNA, IL-6 mRNA, ICAM mRNA and TNF-α mRNA began significantly higher in diabetes group compared with normal group(P＜0.01) on 8th day after induction and remained high expression level throughout the 10-week observation period. However, the expression of CD18 RNA began significantly higher in 4th week after induction and reached peak in 6th week. Conclusions: In conclusion, the retinal microvascular injury, ganglion cell changes and high expression of VEGF-A, VEGF mRNA, IL-6 mRNA, ICAM mRNA, TNF-α mRNA and CD18 mRNA were happened on very early stage. The results offer new insight into the pathogenesis of diabetic retinopathy, and provide novel targets to inhibit the ocular disease.

scholarly journals Fundus Image Analysis to Detect Abnormalities in Diabetic Retinopathy using Computer Aided Design Tools - A Review

2020 ◽  
Vol 9 (6) ◽  
pp. 224-232
Keyword(s):  
Image Processing ◽  
Diabetic Retinopathy ◽  
Early Detection ◽  
Blood Vessels ◽  
Computer Aided Design ◽  
Vision Loss ◽  
Early Stage ◽  
Human Beings ◽  
Fundus Image ◽  
Detection Techniques

Diabetic retinopathy is becoming a major threat to visual loss in human beings. Many researchers are working to develop early detection techniques, which may reduce the risk of vision loss using image-processing techniques like image enhancement and segmentation. Improving the quality of medical images to detect the disease at an early stage is crucial for further medication. It is gaining more focus with automated techniques for machine learning. Filtering and morphological operators enhance image contrast and interested region can be extracted using segmentation techniques from the fundus image of the retina. For feature analysis the optical disk, localization of blood vessels and segmentation are very useful to observe the parameters like area, length and perimeter of blood vessels etc. Algorithms for this analysis include preprocessing, segmentation, feature extraction and classification. This paper tries to give a detailed review of various image-processing methods used in early detection of diabetic retinopathy and future insights to develop algorithms, which reduces clinician’s time for diagnosis and pathogenesis.

scholarly journals Modelling on-demand preprocessing framework towards practical approach in clinical analysis of diabetic retinopathy

2022 ◽  
Vol 12 (1) ◽  
pp. 585
Author(s):  
Prakruthi Mandya Krishnegowda ◽  
Komarasamy Ganesan
Keyword(s):  
Diabetic Retinopathy ◽  
Vision Loss ◽  
Early Stage ◽  
Signal To Noise Ratio ◽  
Clinical Analysis ◽  
Fundus Images ◽  
Aged People ◽  
On Demand ◽  
Fundus Imaging ◽  
Specific Objective

<p>Diabetic retinopathy (DR) refers to a complication of diabetes and a prime cause of vision loss in middle-aged people. A timely screening and diagnosis process can reduce the risk of blindness. Fundus imaging is mainly preferred in the clinical analysis of DR. However; the raw fundus images are usually subjected to artifacts, noise, low and varied contrast, which is very hard to process by human visual systems and automated systems. In the existing literature, many solutions are given to enhance the fundus image. However, such approaches are particular and limited to a specific objective that cannot address multiple fundus images. This paper has presented an on-demand preprocessing frame work that integrates different techniques to address geometrical issues, random noises, and comprehensive contrast enhancement solutions. The performance of each preprocessing process is evaluated against peak signal-to-noise ratio (PSNR), and brightness is quantified in the enhanced image. The motive of this paper is to offer a flexible approach of preprocessing mechanism that can meet image enhancement needs based on different preprocessing requirements to improve the quality of fundus imaging towards early-stage diabetic retinopathy identification.</p>

scholarly journals Identification of Diabetic Retinopathy from fundus images using CNNs

2019 ◽  
Vol 9 (1) ◽  
pp. 3439-3443
Keyword(s):  
Diabetic Retinopathy ◽  
Vision Loss ◽  
Early Stage ◽  
Human Vision ◽  
Learning Approach ◽  
Fundus Images ◽  
Diabetes Treatment ◽  
Proposed Model ◽  
Fine Tune ◽  
Better Than

the Diabetic Retinopathy is the diabetes-mellitus to human vision that is the main cause of vision loss. The early stage detection of diabetic retinopathy is can play eminent role in the diabetes treatment. The fundus of retinal image is utilized to recognize the symptoms of diabetic retinopathy. Moreover, the above phenomena led us to propose this paper; here we propose segment based learning approach for identification of diabetic retinopathy. The segment based image level is required to obtain the identification of diabetic retinopathy images, the classifiers and features are equally learned from the data. Then, we adapt pre-trained CNN as the fine tune to achieve the segment level estimation of diabetic retinopathy. For identification of diabetic retinopathy, we achieve accuracy 96.97 and 98.46% at 20 and 30% and also achieve AUC (Area under Curve) 97.51 and 98.50 at 20 and 30% on the Kaggle dataset. Our proposed model outperforms much better than other models.

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