scholarly journals Glaucoma detection using cup to disc ratio and artificial neural networks

2017 ◽  
Vol 7 (1.5) ◽  
pp. 135 ◽  
Author(s):  
Gayathri R. ◽  
Rao P. V.
Keyword(s):  
Early Stage ◽  
Eye Disease ◽  
Human Beings ◽  
Eye Infections ◽  
Glaucoma Detection ◽  
Artificial Neural ◽  
Handling Methods ◽  
Retinal Fundus ◽  
Cup To Disc Ratio

Now-a-days, the most commonly predicted eye disease in human beings is glaucoma; loss of vision gradually may turn into blindness. Advanced image handling methods empower osteopathic specialist to distinguish and treat a few eye infections like diabetic retinopathy and glaucoma. The pressure in the optic nerve of the eye may lead to get affected by glaucoma, which is most regular reason for visual deficiency of the peoples, if not treated appropriately at early stage. The main objective of this paper is the detection of glaucoma and classifies the disease based on its severity using artificial neural network. In this paper mainly focused on pre -processing of retinal fundus images for improving the quality of detection and easy to further handling. The simulation results to obtain using MATLAB for the better accuracy in detecting glaucoma for abnormality using Cup to Disc ratio of retinal fund us images. 

scholarly journals Vision Atrophy Screening and Revelation

2021 ◽  
Author(s):  
Kiruthiga Devi M ◽  
Lingamuthu K ◽  
Baskar M ◽  
Deepa B ◽  
Merlin G
Keyword(s):  
Optic Disc ◽  
Vision Loss ◽  
Low Cost ◽  
Remote Diagnostics ◽  
Glaucoma Detection ◽  
Screening Services ◽  
Detection And Diagnosis ◽  
Retinal Fundus ◽  
To Receive

Glaucoma which is known as the “thief of sight”, is an irreversible eye disease It is mainly caused by increased intraocular pressure (IOP), or loss of blood supply to the optic nerve. Glaucoma detection and diagnosis is very important. By analyzing the optic disc and its surroundings, This paper introduces a method for providing automated glaucoma screening services based on a framework that proposes a retinal image synthesizer for glaucoma assessment by analyzing the optic disc and its surroundings. The Cup to Disc Ratio (CDR) is critical for the system, and it is calculated using 2-D retinal fundus images. The synthetic images produced by our system are compared quantitatively. The structural properties of synthetic and real images are analyzed, and the quality of colour is calculated by extracting the 2-D histogram. The system allows patients to receive low-cost remote diagnostics from a distance, preventing blindness and vision loss by early detection and management.

Glaucoma Detection Using Convolutional Neural Networks

2021 ◽  
pp. 1-7
Author(s):  
Meet Ganpatlal Oza ◽  
Geeta Rani ◽  
Vijaypal Singh Dhaka
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Intraocular Pressure ◽  
Early Detection ◽  
Early Stage ◽  
Physical Activities ◽  
Eye Disease ◽  
Eye Injuries ◽  
Glaucoma Detection ◽  
Ict Tools

The increase in use of ICT tools and decrease in physical activities has increased the risk of disorders such as diabetes, hypertension, myopia, hypermetropia, etc. These disorders make the person more prone to eye disease such as glaucoma. The actual causes of glaucoma are still unknown. But the study of medical literature reveals that the factors such as intraocular pressure, thyroid, diabetics, eye injuries, eye surgeries, ethnic background, and myopia makes the person more prone to glaucoma. The difficulty in early detection make it an invisible thief of sight. Therefore, it is the demand of the day to design a system for its early detection. The aim of this chapter is to develop a convolutional neural network model “GlaucomaDetector” for detection of glaucoma at an early stage. The evaluation of the model on the publicly available dataset reports the accuracy of 99% for prediction of glaucoma from the input images of retina. This may prove a useful tool for doctors for quick prediction of glaucoma at an early stage. Thus, it can minimize the risk of blindness in patients.

scholarly journals Survey on Automatic Detection of Glaucoma through Deep Learning Using Retinal Fundus Images

2020 ◽  
Vol 7 (4) ◽  
pp. 11-15
Author(s):  
Diwakaran ◽  
S.Sheeba Jeya Sophia
Keyword(s):  
Optic Nerve ◽  
High Efficiency ◽  
Structural Features ◽  
Fundus Images ◽  
Non Invasive ◽  
Glaucoma Detection ◽  
Retinal Fundus Images ◽  
Retinal Fundus ◽  
Isnt Rule ◽  
Cup To Disc Ratio

Glaucoma - a disease which causes damage to our eye's optic nerve and subsequently blinds the vision. This occurs due to increased intraocular pressure (IOP) which causes the damage of optic nerve axons at the back of the eye, with eventual deterioration of vision. Presently, many works have been done towards automatic glaucoma detection using Fundus Images (FI) by extracting structural features. Structural features can be extracted from optic nerve head (ONH) analysis, cup to disc ratio (CDR) and Inferior, Superior, Nasal, Temporal (ISNT) rule in Fundus Image for glaucoma assessment.This survey presents various techniques for the early detection of glaucoma. Among the various techniques, retinal image-based detection plays a major role as it comes under non-invasive methods of detection. Here, a review and study were conducted for the different techniques of glaucoma detection using retinal fundus images. The objective of this survey is to obtain a technique which automatically analyze the retinal images of the eye with high efficiency and accuracy

scholarly journals An Adoptive Threshold-Based Multi-Level Deep Convolutional Neural Network for Glaucoma Eye Disease Detection and Classification

Diagnostics ◽  
2020 ◽  
Vol 10 (8) ◽  
pp. 602
Author(s):  
Muhammad Aamir ◽  
Muhammad Irfan ◽  
Tariq Ali ◽  
Ghulam Ali ◽  
Ahmad Shaf ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Deep Convolutional Neural Network ◽  
Eye Disease ◽  
Fundus Images ◽  
Glaucoma Detection ◽  
Multi Level ◽  
Retinal Fundus Images ◽  
And Performance ◽  
Retinal Fundus

Glaucoma, an eye disease, occurs due to Retinal damages and it is an ordinary cause of blindness. Most of the available examining procedures are too long and require manual instructions to use them. In this work, we proposed a multi-level deep convolutional neural network (ML-DCNN) architecture on retinal fundus images to diagnose glaucoma. We collected a retinal fundus images database from the local hospital. The fundus images are pre-processed by an adaptive histogram equalizer to reduce the noise of images. The ML-DCNN architecture is used for features extraction and classification into two phases, one for glaucoma detection known as detection-net and the second one is classification-net used for classification of affected retinal glaucoma images into three different categories: Advanced, Moderate and Early. The proposed model is tested on 1338 retinal glaucoma images and performance is measured in the form of different statistical terms known as sensitivity (SE), specificity (SP), accuracy (ACC), and precision (PRE). On average, SE of 97.04%, SP of 98.99%, ACC of 99.39%, and PRC of 98.2% are achieved. The obtained outcomes are comparable to the state-of-the-art systems and achieved competitive results to solve the glaucoma eye disease problems for complex glaucoma eye disease cases.

scholarly journals Glaucoma Detection using SVM Classifier

2020 ◽  
Vol 9 (7) ◽  
pp. 336-339
Keyword(s):  
Intelligent System ◽  
Principal Component ◽  
Optic Disk ◽  
Eye Disease ◽  
Svm Classifier ◽  
Fundus Images ◽  
Artificial Intelligent ◽  
Glaucoma Detection ◽  
Retinal Fundus Images ◽  
Retinal Fundus

Glaucoma is a autistic eye disease and major causes of firm blindness worldwide. For this we are trying to design a tool for early detection of glaucoma. In this paper glaucoma detection is based on the algorithm of retinal fundus images[1]. A supervised techniques for the detection of glaucoma is used. For the extraction of the features of the images we used PCA(principal component analysis). And for the classification support vectors are used. It shows mainly an artificial intelligent system for the segmentation of optic disk and cup. The accuracy of this model is comparatively much more greater than previously designed neural architectures

An Efficient Hybrid Forecasting Approach for Wind Speed Time Series

2017 ◽  
Vol 7 (9) ◽  
pp. 13
Author(s):  
Bhargavi Munnaluri ◽  
K. Ganesh Reddy
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Wind Speed ◽  
Fossil Fuels ◽  
Renewable Energy Sources ◽  
Support Vector ◽  
Hybrid Forecasting ◽  
Artificial Neural ◽  
Future Utilization

Wind forecasting is one of the best efficient ways to deal with the challenges of wind power generation. Due to the depletion of fossil fuels renewable energy sources plays a major role for the generation of power. For future management and for future utilization of power, we need to predict the wind speed.  In this paper, an efficient hybrid forecasting approach with the combination of Support Vector Machine (SVM) and Artificial Neural Networks(ANN) are proposed to improve the quality of prediction of wind speed. Due to the different parameters of wind, it is difficult to find the accurate prediction value of the wind speed. The proposed hybrid model of forecasting is examined by taking the hourly wind speed of past years data by reducing the prediction error with the help of Mean Square Error by 0.019. The result obtained from the Artificial Neural Networks improves the forecasting quality.

Predictive performance modeling of Habesha brewery wastewater treatment plant using artificial neural networks

2019 ◽  
Author(s):  
Chem Int
Keyword(s):  
Neural Networks ◽  
Water Quality ◽  
Wastewater Treatment ◽  
Artificial Neural Networks ◽  
Performance Modeling ◽  
Predictive Performance ◽  
Mathematical Tool ◽  
Effluent Water ◽  
Artificial Neural

Recently, process control in wastewater treatment plants (WWTPs) is, mostly accomplished through examining the quality of the water effluent and adjusting the processes through the operator’s experience. This practice is inefficient, costly and slow in control response. A better control of WTPs can be achieved by developing a robust mathematical tool for performance prediction. Due to their high accuracy and quite promising application in the field of engineering, Artificial Neural Networks (ANNs) are attracting attention in the domain of WWTP predictive performance modeling. This work focuses on applying ANN with a feed-forward, back propagation learning paradigm to predict the effluent water quality of the Habesha brewery WTP. Data of influent and effluent water quality covering approximately an 11-month period (May 2016 to March 2017) were used to develop, calibrate and validate the models. The study proves that ANN can predict the effluent water quality parameters with a correlation coefficient (R) between the observed and predicted output values reaching up to 0.969. Model architecture of 3-21-3 for pH and TN, and 1-76-1 for COD were selected as optimum topologies for predicting the Habesha Brewery WTP performance. The linear correlation between predicted and target outputs for the optimal model architectures described above were 0.9201 and 0.9692, respectively.

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