scholarly journals Klasifikasi Fase Retinopati Diabetes Menggunakan Backpropagation Neural Network

2013 ◽  
Vol 7 (1) ◽  
pp. 23
Author(s):  
Rocky Yefrenes Dillak ◽  
Agus Harjoko
Keyword(s):  
Neural Network ◽  
Diabetic Retinopathy ◽  
Macular Edema ◽  
Proliferative Diabetic Retinopathy ◽  
Texture Features ◽  
Fundus Images ◽  
Gray Level ◽  
Backpropagation Neural Network ◽  
Maximum Probability ◽  
Keywords Diabetic Retinopathy

AbstrakRetinopati diabetes (DR) merupakan salah satu komplikasi pada retina yang disebabkan oleh penyakit diabetes. Tingkat keparahan DR dibagi atas empat kelas yakni: normal, non-proliferative diabetic retinopathy (NPDR), proliferative diabetic retinopathy (PDR), dan macular edema (ME). Penelitian ini bertujuan mengembangkan suatu metode yang dapat digunakan untuk melakukan klasifikasi terhadap fase DR. Data yang digunakan sebanyak 97 citra yang fitur – fiturnya  diekstrak menggunakan gray level cooccurence matrix (GLCM). Fitur ciri tersebut adalah maximum probability, correlation, contrast, energy, homogeneity, dan entropy. Fitur – fitur ini dilatih menggunakan jaringan syaraf tiruan backpropagation untuk dilakukan klasifikasi. Kinerja  yang dihasilkan dari pendekatan ini adalah sensitivity 100%, specificity 100% dan accuracy  97.73%  Kata kunci— fase retinopati diabetes, GLCM, backpropagation neural network  Abstract Diabetic retinopathy (DR) is one of the complications on retina caused by diabetes. The aim of this studyis to develop a system that can be used for automatic mass screenings of diabetic retinopathy. Four classes are identified: normal retina, non-proliferative diabetic retinopathy (NPDR), proliferative diabetic retinopathy (PDR), and macular edema (ME). Ninenty-seven retinal fundus images in used in this study. Six different texture features such as maximum probability, correlation, contrast, energy, homogeneity, and entropy were extracted from the digital fundus images using gray level cooccurence matrix (GLCM). These features were fed into a backpropagation neural network classifier for automatic classification. The  proposed approach is able to classify with sensitivity 100%, specificity 100% and accuracy  97.73%  Keywords— diabetic retinopathy stages, GLCM,  backpropagation neural network

scholarly journals Detection of Proliferative Diabetic Retinopathy in Fundus Images Using Convolution Neural Network

2020 ◽  
Vol 769 ◽  
pp. 012029
Author(s):  
Hasliza Abu Hassan ◽  
Marzuqi Yaakob ◽  
Sasni Ismail ◽  
Juwairiyyah Abd Rahman ◽  
Izyani Mat Rusni ◽  
...  
Keyword(s):  
Neural Network ◽  
Diabetic Retinopathy ◽  
Proliferative Diabetic Retinopathy ◽  
Convolution Neural Network ◽  
Fundus Images

Computerized Diabetic Retinopathy Diagnosis with Optimized Random Forest

2019 ◽  
Vol 9 (2) ◽  
pp. 274-283
Author(s):  
Xiaobo Lai ◽  
LV Lili ◽  
Zihe Huang
Keyword(s):  
Diabetic Retinopathy ◽  
Random Forest ◽  
Proliferative Diabetic Retinopathy ◽  
Screening Program ◽  
Early Stage ◽  
Treatment Plan ◽  
Texture Features ◽  
Cost Effective ◽  
Fundus Images ◽  
Diabetic Retinopathy Screening

It is desirable to diagnose diabetic retinopathy at an early stage for developing a suitable treatment plan to prevent the condition from deteriorating. To provide an immediate diagnosis of the retina, various methods have been investigated to realize a time and cost effective classification of the fundus images. However, most diabetic retinopathy automated identification methods are structural based analysis. Moreover, Asian fundus images have larger optic disc and thicker retinal vessels compared with Caucasians. Hence, we explore a machine learning approach to the extraction of texture features for classification and the feasibility of this approach using texture parameters to complement current algorithms. Normal retina, non-proliferative diabetic retinopathy and proliferative diabetic retinopathy are identified in this paper. The first step is achieved with three groups of texture features such as gray level co-occurrence matric texture features, different statistical features and run length matrix texture features extracted. In the second step, these features are fed into an optimized random forest classifier for automatic classification. We test our system on two databases (D1 and D2) consisting of 432 and 579 fundus images from a diabetic retinopathy screening program consisting of Asians. The diabetic retinopathy is successfully diagnosed with sensitivity is 0.936 ± 0.019 for D1 and 0.941 ± 0.016 for D2, specificity is 0.917 ± 0.011 for D1 and 0.918 ± 0.011 for D2, positive predictive value is 0.924 ± 0.013 for D1 and 0.939 ± 0.012 for D2, when training on the same institutions, respectively.

scholarly journals ANN and SVM to recognize Texture features for spontaneous Detection and Rating of Diabetic Retinopathy

2019 ◽  
Vol 9 (1) ◽  
pp. 5590-5595
Keyword(s):  
Diabetic Retinopathy ◽  
Research Work ◽  
Texture Features ◽  
Support Vector ◽  
Fundus Images ◽  
Small Vessels ◽  
The Neural Network ◽  
Major Loss ◽  
Occurrence Matrix ◽  
Fluid Leak

The higher levels of blood glucose most often causes a metabolic disorder commonly called as Diabetes, scientifically as Diabetes Mellitus. A consequence of this is a major loss of vision and in long terms may eventually cause complete blindness. It initiates with swelling on blood vessels, formation of microaneurysms at the end of narrow capillaries. Haemorrhages due to rupture of small vessels and fluid leak causes exudates. The specialist examines it to diagnose and gives proper treatment. Fundus images are the fundamental tool for proper diagnosis of patients by medical experts. In this research work the fundus images are taken for processing, the neural network and support vector machine are trained for the proposed model. The features are extracted from the diabetic retinopathy image by using texture based algorithms such as Gabor, Local binary pattern and Gray level co-occurrence matrix for rating the level of diabetic retinopathy. The performance of all methods is calculated based on accuracy, precision, Recall and f-measure.

scholarly journals Treatment of cystoid macular edema in a patient with primary open angle glaucoma and diabetic retinopathy by withdrawal of travoprost

2021 ◽  
Vol 7 (2) ◽  
pp. 180-183
Author(s):  
Shiv Sagar N ◽  
BN Kalpana ◽  
Shilpa YD
Keyword(s):  
Diabetic Retinopathy ◽  
Macular Edema ◽  
Laser Treatment ◽  
Proliferative Diabetic Retinopathy ◽  
Cystoid Macular Edema ◽  
Open Angle Glaucoma ◽  
Eye Drops ◽  
Antiglaucoma Medication ◽  
Micropulse Laser

To study the association of cystoid macular edema (CME) and Travoprost eye drops in a patient with diabetic retinopathy (DR).The study was carried out on a 65yr old patient on a regular follow up from 2009-2018.A 65yr old patient of a DR of both eyes who had received 3 sittings of pan retinal photocoagulation (PRP) laser in both eyes and grid laser to his right eye. He was on regular follow up since 2009 with a stable proliferative diabetic retinopathy (PDR). Patient was also on topical antiglaucoma medication and had prophylactic YAG-PI done both eyes. He was on regular follow up since 2009 with a stable proliferative diabetic retinopathy (PDR). Right eye showed macular edema (ME) in 2014 and underwent OCT and FFA. Patient refused for intravitreal injection and preferred laser treatment, so patient underwent micropulse laser treatment in 2014. His edema persisted even after micropulse treatment. His systemic control was good and patient continued to use Travoprost eye drops. So in 2017 suspected CME secondary to topical prostaglandin (PG) analogue as he had strict glycemic control and was no fluctuation in ME. Hence topical PG analogue was withdrawn and stopped. On subsequent follow up after 2 months CME had completely disappeared and the foveal contour returned to normal on OCT. LE was status quo. Patient was followed up for more than 1 year and continuously followed up, 15 days back in June 2018 had no evidence of CME and vision was 6/9 in both eyes.: Differentiation of DME and CME secondary to PG analogue should be made at the earliest.

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.

An Approach for Automatic Detection and Grading of Macular Edema

2017 ◽  
pp. 1677-1702
Author(s):  
Jyoti Prakash Medhi
Keyword(s):  
Image Processing ◽  
Decision Making ◽  
Diabetic Retinopathy ◽  
Blood Vessel ◽  
Macular Edema ◽  
Automatic Detection ◽  
Automatic Analysis ◽  
Fundus Images

Prolonged Diabetes causes massive destruction to the retina, known as Diabetic Retinopathy (DR) leading to blindness. The blindness due to DR may consequence from several factors such as Blood vessel (BV) leakage, new BV formation on retina. The effects become more threatening when abnormalities involves the macular region. Here automatic analysis of fundus images becomes important. This system checks for any abnormality and help ophthalmologists in decision making and to analyze more number of cases. The main objective of this chapter is to explore image processing tools for automatic detection and grading macular edema in fundus images.

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