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A Review on Computer Vision based Classification of Diabetic Retinopathy using Artificial Intelligence
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IOSR Journals
Diabetic retinopathy is a complication of diabetes causing progressive damage to the retina, located at the back of the eye, potentially leading to clouded vision or blindness. Disease signs may be visualized by Optical Coherence Tomography (OCT) and include formation of new and weaker blood vessels, fluid accumulation, exudates and changes to Retinal Vascular Geometry (RVG). Presence of these indicators can provide information as to the stage of the disease. Image-processing strategies are applied for the automated detection, segmentation, extraction, classification toward likelihood estimation of progression of diabetic retinopathy to visual biomarkers present in OCT, using time-sequenced data in the early stages of the disease. Gabor and Savitsky-Golay filtering enables extraction of the vessel map and fuzzy control for segmentation of hard exudates. Feature data are extracted using bounding boxes, vector map and connected component methodology for binary decision tree classifier construction, training and testing. Feature values comprising classifier nodes include: exudate features of compactness, area, convexity and form factor, in addition to vessel features: width, elongation, bifurcation angles, form factor and solidity. Classifier accuracy is 93.3%, with 6.7% misclassification and 0% false-negative classification. Automated image processing of diabetic retinopathy is achieved with high classification accuracy for the extraction of vessel map and hard exudate biomarkers from OCT. Application of smoothing algorithms and removal of vessel map shadows may further improve classification accuracy.
IJET-V3I2P4
IJET-V3I2P4
IJET - International Journal of Engineering and Techniques
Diabetic Retinopathy
diabetic retinopathy.pptx
diabetic retinopathy.pptx
Komal Naphade
To diagnosis of Diabetic Retinopathy (DR) it is the prime cause of blindness in the working age population of the world. Detection method is proposed to detect dark or red lesions such as microaneurysms and hemorrhages in fundus images.Developed during this work, this first is for collection of lesion data information and was used by the ophthalmologist in marking images for database while the automatic diagnosing and displaying the diagnosis result in a more friendly user interface and is as shown in chapter three of this report. The primary aim of this project is to develop a system that will be able to identify patients with BDR and PDR from either colour image or grey level image obtained from the retina of the patient. The algorithm was tested fundus images. The Operating Characteristics (ROC) was determined for red spot lesion and bleeding, while cross over points were only detected leaving further classification as part of future work needed to complete this global project. Sensitivity and specificity was calculated for the algorithm is given respectively as 96.3% and 95.1%
Automatic Detection of Non-Proliferative Diabetic Retinopathy Using Fundus Im...
Automatic Detection of Non-Proliferative Diabetic Retinopathy Using Fundus Im...
iosrjce
https://irjet.net/archives/V4/i7/IRJET-V4I7152.pdf
Diagnosis of Diabetic Retinopathy by Detection of Microneurysm And Exudates
Diagnosis of Diabetic Retinopathy by Detection of Microneurysm And Exudates
IRJET Journal
Similar to A Review on Computer Vision based Classification of Diabetic Retinopathy using Artificial Intelligence
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Diabetic Retinopathy Detection System from Retinal Images
Diabetic Retinopathy Detection System from Retinal Images
IRJET- Detection of Diabetic Retinopathy using Convolutional Neural Network
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Rapid detection of diabetic retinopathy in retinal images: a new approach usi...
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A REVIEW PAPER ON THE DETECTION OF DIABETIC RETINOPATHY
A REVIEW PAPER ON THE DETECTION OF DIABETIC RETINOPATHY
SYSTEM BASED ON THE NEURAL NETWORK FOR THE DIAGNOSIS OF DIABETIC RETINOPATHY
SYSTEM BASED ON THE NEURAL NETWORK FOR THE DIAGNOSIS OF DIABETIC RETINOPATHY
Detection of Diabetic Retinopathy using Kirsch Edge Detection and Watershed T...
Detection of Diabetic Retinopathy using Kirsch Edge Detection and Watershed T...
Discovering Abnormal Patches and Transformations of Diabetics Retinopathy in ...
Discovering Abnormal Patches and Transformations of Diabetics Retinopathy in ...
DISCOVERING ABNORMAL PATCHES AND TRANSFORMATIONS OF DIABETICS RETINOPATHY IN ...
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An Approach for the Detection of Vascular Abnormalities in Diabetic Retinopathy
An Approach for the Detection of Vascular Abnormalities in Diabetic Retinopathy
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How AI Enhances & Accelerates Diabetic Retinopathy Detection
How AI Enhances & Accelerates Diabetic Retinopathy Detection
IRJET- Automatic Detection of Blood Vessels and Classification of Retinal...
IRJET- Automatic Detection of Blood Vessels and Classification of Retinal...
IRJET- Automatic Detection of Blood Vessels and Classification of Retinal Ima...
IRJET- Automatic Detection of Blood Vessels and Classification of Retinal Ima...
C LASSIFICATION O F D IABETES R ETINA I MAGES U SING B LOOD V ESSEL A REAS
C LASSIFICATION O F D IABETES R ETINA I MAGES U SING B LOOD V ESSEL A REAS
DIABETIC RETINOPATHY DETECTION USING MACHINE LEARNING TECHNIQUE
DIABETIC RETINOPATHY DETECTION USING MACHINE LEARNING TECHNIQUE
C018121117
C018121117
IJET-V3I2P4
IJET-V3I2P4
diabetic retinopathy.pptx
diabetic retinopathy.pptx
Automatic Detection of Non-Proliferative Diabetic Retinopathy Using Fundus Im...
Automatic Detection of Non-Proliferative Diabetic Retinopathy Using Fundus Im...
Diagnosis of Diabetic Retinopathy by Detection of Microneurysm And Exudates
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Recently uploaded
Blood Donation Management System is a web database application that enables the public to make online session reservation, to view nationwide blood donation events online and at the same time provides centralized donor and blood stock database. This application is developed by using ASP.NET technology from Visual Studio with the MySQL 5.0 as the database management system. The methodology used to develop this system as a whole is Object Oriented Analysis and Design; whilst, the database for BDMS is developed by following the steps in Database Life Cycle. The targeted users for this application are the public who is eligible to donate blood ,'system moderator, administrator from National Blood Center and the staffs who are working in the blood banks of the participating hospitals. The main objective of the development of this application is to overcome the problems that exist in the current system, which are the lack of facilities for online session reservation and online advertising on the nationwide blood donation events, and also decentralized donor and blood stock database. Besides, extra features in the system such as security protection by using password, generating reports, reminders of blood stock shortage and workflow tracking can even enhance the efficiency of the management in the blood banks. The final result of this project is the development of web database application, which is the BDMS.
Online blood donation management system project.pdf
Online blood donation management system project.pdf
Kamal Acharya
CFD analysis is incredibly effective at solving mysteries and improving the performance of complex systems! Here's a great example: At a large natural gas-fired power plant, where they use waste heat to generate steam and energy, they were puzzled that their boiler wasn't producing as much steam as expected. R&R and Tetra Engineering Group Inc. were asked to solve the issue with reduced steam production. An inspection had shown that a significant amount of hot flue gas was bypassing the boiler tubes, where the heat was supposed to be transferred. R&R Consult conducted a CFD analysis, which revealed that 6.3% of the flue gas was bypassing the boiler tubes without transferring heat. The analysis also showed that the flue gas was instead being directed along the sides of the boiler and between the modules that were supposed to capture the heat. This was the cause of the reduced performance. Based on our results, Tetra Engineering installed covering plates to reduce the bypass flow. This improved the boiler's performance and increased electricity production. It is always satisfying when we can help solve complex challenges like this. Do your systems also need a check-up or optimization? Give us a call! Work done in cooperation with James Malloy and David Moelling from Tetra Engineering. More examples of our work https://www.r-r-consult.dk/en/cases-en/
CFD Simulation of By-pass Flow in a HRSG module by R&R Consult.pptx
CFD Simulation of By-pass Flow in a HRSG module by R&R Consult.pptx
R&R Consult
fluid mechanics notes by devender poonia sir
fluid mechanics gate notes . gate all pyqs answer
fluid mechanics gate notes . gate all pyqs answer
apareshmondalnita
ASME IX 2007 Full
ASME IX(9) 2007 Full Version .pdf
ASME IX(9) 2007 Full Version .pdf
AhmedHussein950959
NO1 Pandit Amil Baba In Bahawalpur, Sargodha, Sialkot, Sheikhupura, Rahim Yar Khan, Jhang, Dera Ghazi Khan, Gujrat +92322-6382012
NO1 Pandit Amil Baba In Bahawalpur, Sargodha, Sialkot, Sheikhupura, Rahim Yar...
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Amil baba
Scaling in conventional MOSFET
Scaling in conventional MOSFET for constant electric field and constant voltage
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RCC Institute of Information Technology
This portfolio contains selected projects I completed during my undergraduate studies. 2018 - 2023
Architectural Portfolio Sean Lockwood
Architectural Portfolio Sean Lockwood
seandesed
Laundry firms currently use a manual system for the management and maintenance of critical information. The current system requires numerous paper forms, with data stores spread throughout the laundry management infrastructure. Often information is incomplete or does not follow management standards. Records are often lost in transit during computation requiring a comprehensive auditing process to ensure that no vital information is lost. Multiple copies of the same information exist in the laundry firm data and may lead to inconsistencies in data in various data stores. A significant part of the operation of any laundry firm involves the acquisition, management and timely retrieval of great volumes of information. This information typically involves; customer personal information and clothing records history, user information, price of delivery and received date, users scheduling as regards customers details and dealings in service rendered, also our products package waiting list. All of this information must be managed in an efficient and cost wise fashion so that the organization resources may be effectively utilized. We present the design and implementation of a laundry database management system (LBMS) used in a laundry establishment. Laundry firms are usually faced with difficulties in keeping detailed records of customers clothing; this little problem as seen to most laundry firms is highly discouraging as customers are filled with disappointments, arising from issues such as customer clothes mix-ups and untimely retrieval of clothes. The aim of this application is to determine the number of clothes collected, in relation to their owners, as this also helps the users fix a date for the collection of their clothes. Also customer’s information is secured, as a specific id is allocated per registration to avoid contrasting information.
Laundry management system project report.pdf
Laundry management system project report.pdf
Kamal Acharya
about waif
LIGA(E)11111111111111111111111111111111111111111.ppt
LIGA(E)11111111111111111111111111111111111111111.ppt
ssuser9bd3ba
Overview of the fundamental roles in Hydropower generation and the components involved in wider Electrical Engineering. This paper presents the design and construction of hydroelectric dams from the hydrologist’s survey of the valley before construction, all aspects and involved disciplines, fluid dynamics, structural engineering, generation and mains frequency regulation to the very transmission of power through the network in the United Kingdom. Author: Robbie Edward Sayers Collaborators and co editors: Charlie Sims and Connor Healey. (C) 2024 Robbie E. Sayers
HYDROPOWER - Hydroelectric power generation
HYDROPOWER - Hydroelectric power generation
Robbie Edward Sayers
fundamentals of drawing and difference between isometric and orthographic projection. Basic representation principles.
fundamentals of drawing and isometric and orthographic projection
fundamentals of drawing and isometric and orthographic projection
jeevanprasad8
The export maintenance system is a fully featured application that can help we manage fruit delivery business and achieve more control and information at a very low cost of total ownership. A fruit export maintains automatically monitors purchase, sales, supplier information. The system includes receiving fruit from the different supplier. Customer order is placed in the system, based on the order fruit has been sales to the customer. The report contains the details about product, purchase, sales, stock, and invoice. The main objective of this project is to computerize the company activities and to provide details about the production process at the fruit export maintenance system. The demand of fresh fruit fruits and processed food items in international and domestic market has shown a decent increase. This estimation is creating a necessity for growing more and more fruit fruits to cater the growing demand of domestic & international market. The customers effectively and hence help for establishing good relation between customer and fruit shop organization. It contains various customized modules for effectively maintaining fruit and stock information accurately and safely. When the fruits are sold to the customer, stock will be reduced automatically. When a new purchase is made, stock will be increased automatically. While selecting fruits for sale, the proposed software will automatically check for total number of available stock of that particular item, if the total stock of that particular item is less than 5, software will notify the user to purchase the particular item. The proposed project is developed to manage the fruit shop in the fruits for shop. The first module is the login. The admin should login to the project for usage. The username and password are verified and if it is correct, next form opens. If the username and password are not correct, it shows the error message.
Fruit shop management system project report.pdf
Fruit shop management system project report.pdf
Kamal Acharya
Used in finite element analysis
shape functions of 1D and 2 D rectangular elements.pptx
shape functions of 1D and 2 D rectangular elements.pptx
VishalDeshpande27
This paper addresses the vulnerability of deep learning models, particularly convolutional neural networks (CNN)s, to adversarial attacks and presents a proactive training technique designed to counter them. We introduce a novel volumization algorithm, which transforms 2D images into 3D volumetric representations. When combined with 3D convolution and deep curriculum learning optimization (CLO), itsignificantly improves the immunity of models against localized universal attacks by up to 40%. We evaluate our proposed approach using contemporary CNN architectures and the modified Canadian Institute for Advanced Research (CIFAR-10 and CIFAR-100) and ImageNet Large Scale Visual Recognition Challenge (ILSVRC12) datasets, showcasing accuracy improvements over previous techniques. The results indicate that the combination of the volumetric input and curriculum learning holds significant promise for mitigating adversarial attacks without necessitating adversary training.
Immunizing Image Classifiers Against Localized Adversary Attacks
Immunizing Image Classifiers Against Localized Adversary Attacks
gerogepatton
Introduction to Machine Learning Notes
Introduction to Machine Learning Unit-4 Notes for II-II Mechanical Engineering
Introduction to Machine Learning Unit-4 Notes for II-II Mechanical Engineering
C Sai Kiran
About Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol. • Remote control: Parallel or serial interface. • Compatible with MAFI CCR system. • Compatible with IDM8000 CCR. • Compatible with Backplane mount serial communication. • Compatible with commercial and Defence aviation CCR system. • Remote control system for accessing CCR and allied system over serial or TCP. • Indigenized local Support/presence in India. • Easy in configuration using DIP switches. Technical Specifications Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol. Key Features Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol. • Remote control: Parallel or serial interface • Compatible with MAFI CCR system • Copatiable with IDM8000 CCR • Compatible with Backplane mount serial communication. • Compatible with commercial and Defence aviation CCR system. • Remote control system for accessing CCR and allied system over serial or TCP. • Indigenized local Support/presence in India. Application • Remote control: Parallel or serial interface. • Compatible with MAFI CCR system. • Compatible with IDM8000 CCR. • Compatible with Backplane mount serial communication. • Compatible with commercial and Defence aviation CCR system. • Remote control system for accessing CCR and allied system over serial or TCP. • Indigenized local Support/presence in India. • Easy in configuration using DIP switches.
Standard Reomte Control Interface - Neometrix
Standard Reomte Control Interface - Neometrix
Neometrix_Engineering_Pvt_Ltd
ENERGY STORAGE DEVICES
ENERGY STORAGE DEVICES INTRODUCTION UNIT-I
ENERGY STORAGE DEVICES INTRODUCTION UNIT-I
VigneshvaranMech
This is a assigned group presentation given by my Computer Science course teacher at Green University of Bangladesh, Bangladesh. My Presentation Topic was - Cloud Computing This group presentation includes the work Md. Shahidul Islam Prodhan, pages no 10 - 15. www.facebook.com/TheShahidul www.twitter.com/TheShahidul www.linkedin.com/TheShahidul
Cloud-Computing_CSE311_Computer-Networking CSE GUB BD - Shahidul.pptx
Cloud-Computing_CSE311_Computer-Networking CSE GUB BD - Shahidul.pptx
Md. Shahidul Islam Prodhan
Presented at NUS: Fuzzing and Software Security Summer School 2024 This keynote talks about the democratization of fuzzing at scale, highlighting the collaboration between open source communities, academia, and industry to advance the field of fuzzing. It delves into the history of fuzzing, the development of scalable fuzzing platforms, and the empowerment of community-driven research. The talk will further discuss recent advancements leveraging AI/ML and offer insights into the future evolution of the fuzzing landscape.
Democratizing Fuzzing at Scale by Abhishek Arya
Democratizing Fuzzing at Scale by Abhishek Arya
abh.arya
read it
Digital Signal Processing Lecture notes n.pdf
Digital Signal Processing Lecture notes n.pdf
AbrahamGadissa
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Online blood donation management system project.pdf
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fluid mechanics gate notes . gate all pyqs answer
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ASME IX(9) 2007 Full Version .pdf
ASME IX(9) 2007 Full Version .pdf
NO1 Pandit Amil Baba In Bahawalpur, Sargodha, Sialkot, Sheikhupura, Rahim Yar...
NO1 Pandit Amil Baba In Bahawalpur, Sargodha, Sialkot, Sheikhupura, Rahim Yar...
Scaling in conventional MOSFET for constant electric field and constant voltage
Scaling in conventional MOSFET for constant electric field and constant voltage
Architectural Portfolio Sean Lockwood
Architectural Portfolio Sean Lockwood
Laundry management system project report.pdf
Laundry management system project report.pdf
LIGA(E)11111111111111111111111111111111111111111.ppt
LIGA(E)11111111111111111111111111111111111111111.ppt
HYDROPOWER - Hydroelectric power generation
HYDROPOWER - Hydroelectric power generation
fundamentals of drawing and isometric and orthographic projection
fundamentals of drawing and isometric and orthographic projection
Fruit shop management system project report.pdf
Fruit shop management system project report.pdf
shape functions of 1D and 2 D rectangular elements.pptx
shape functions of 1D and 2 D rectangular elements.pptx
Immunizing Image Classifiers Against Localized Adversary Attacks
Immunizing Image Classifiers Against Localized Adversary Attacks
Introduction to Machine Learning Unit-4 Notes for II-II Mechanical Engineering
Introduction to Machine Learning Unit-4 Notes for II-II Mechanical Engineering
Standard Reomte Control Interface - Neometrix
Standard Reomte Control Interface - Neometrix
ENERGY STORAGE DEVICES INTRODUCTION UNIT-I
ENERGY STORAGE DEVICES INTRODUCTION UNIT-I
Cloud-Computing_CSE311_Computer-Networking CSE GUB BD - Shahidul.pptx
Cloud-Computing_CSE311_Computer-Networking CSE GUB BD - Shahidul.pptx
Democratizing Fuzzing at Scale by Abhishek Arya
Democratizing Fuzzing at Scale by Abhishek Arya
Digital Signal Processing Lecture notes n.pdf
Digital Signal Processing Lecture notes n.pdf
A Review on Computer Vision based Classification of Diabetic Retinopathy using Artificial Intelligence
1.
International Research Journal
of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 09 Issue: 08 | Aug 2022 www.irjet.net p-ISSN: 2395-0072 © 2022, IRJET | Impact Factor value: 7.529 | ISO 9001:2008 Certified Journal | Page 240 A Review on Computer Vision based Classification of Diabetic Retinopathy using Artificial Intelligence 1Mr. Satish D. Kale, 2Dr. S. B. More, 1PG Student, 2Professor, 1-2Department of Computer Engineering, 1-2Aditya Engineering College, Beed, Maharashtra, India. ---------------------------------------------------------------------***--------------------------------------------------------------------- Abstract - Diabetic retinopathy (DR) is a retinal condition that affects people with diabetes and is the leading cause of blindness among the elderly. Changes in blood vessels might lead them to bleed or leak fluid, producingvisual distortion. As a result, blood vessel extraction is critical in assisting ophthalmologists in early detection of this condition and preventing vision loss. DiabetesRetinopathy isaseverechronic condition that is one of the primary causes of blindness and visual impairment among diabetic individuals in affluent nations. According to studies, 90 percent of instances may be avoided with early identification and treatment. Physicians utilize retinal imaging to detect lesions associated with this illness during eye screening. The amount of photos that must be manually evaluated is growing costly because to the rising number of diabetics. Furthermore, training new staff for this form of image-based diagnosis takes a long time because it requires daily practice to gain skill. The review of retinopathy categorization for diabetic patients is discussed in this research utilizing several approaches using computer vision i.e. image processing with artificial intelligence. Key Words: Artificial Intelligence, Computer Vision, Diabetic Retinopathy, Machine Learning, Deep Learning 1. INTRODUCTION Diabetic Retinopathy (DR) is human eye disease among people with diabetics which causes damage to retina of eye and may eventually lead to complete blindness. Diabetes mellitus is a metabolic disorder characterized by a hyper- glycaemia due to malfunction in the production of insulin by the pancreas. At long term, it can cause microvascular complications that affect the retina, resulting in Diabetic Retinopathy (DR), which is the leading cause of blindness in active population. Moreover, the World Health Organization (WHO) anticipates that 347 million people were diagnosed with diabetes in the world, and it is predicted that, can be affect more than 640 million people by 2040. According to some estimations, more than 75%ofdiabetic patientswithin 15 to 20 years of diabetes diagnosis are endangered by DR. Diabetic retinopathy is an asymptomatic retinal disease and primarily a consequence of diabetes,whichinvolveschanges to blood vessels,resultingin microaneurysms,hemorrhages, exudates, malformation and vascular tortuosity (Non- Proliferative Diabetic Retinopathy) that can subsequently cause an abnormal growth of retinal blood vessels (Proliferative Diabetic Retinopathy) that can lead to blindness in the absence of appropriate treatment. Therefore, the extraction of blood vessels is crucial to help ophthalmologists to identify this disease at the earlystage in order to prevent the loss of vision. Anatomy of eye for normal retina and DR-affected retina is shown in Fig-1 and Fig-2 respectively [1] [2]. Diabetes is a condition in which glucose metabolism is disrupted, resulting in a variety of problems. Diabetic retinopathy (DR) is a disorder in which blood vessels in the rear of the retina get damaged. According to the International Diabetes Federation (IDF), approximately million people worldwide have diabetes, and roughly one- third of them have indications of DR. No DR, Mild, Moderate, Severe, and Proliferative DR are the five stages of DR based on severity, as seen in the retinal fundus photography photographs or retinal fundus images in figure 3. Furthermore, later phases of DR are marked by the creation of aberrant blood vessels, known as neovascularization. DR can be effectively managed in the early stages, however DR detected at later stages may cause irreversible loss of vision. According to the Early Treatment Diabetic Retinopathy Study (ETDRS), the Diabetic Retinopathy(DR)risk levelsare listed in Table 1 and their visual representation at different stages as shown in fig3. Fig-1: Normal Retina Fig-2: DR-affected Retina
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International Research Journal
of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 09 Issue: 08 | Aug 2022 www.irjet.net p-ISSN: 2395-0072 © 2022, IRJET | Impact Factor value: 7.529 | ISO 9001:2008 Certified Journal | Page 241 Fig3: Stages of Diabetic Retinopathy Table 1: Diabetic Retinopathy risk levels DR Risk level Lesions No DR No lesions Mild NPDR Presence of MA Moderate NPDR Presence of MA and HM Presence of Cotton wool spots and Exudates Severe NPDR Any of the symptoms Venous beading in 2 quadrants Presence of MA and extensive HM in 4 quadrants Intraretinal microvascular abnormalities in 1 quadrant PDR Neovascularization Presence of preretinal & vitreous HM Ophthalmologists urge diabetic people to have their fundus medically screened on a regular basis to detect DRs early. Nonetheless, diabetic retinopathies are often overlooked until significant damage to the patient's fundushasoccurred (typically manifested as worsening or loss of vision). The proper identification and categorization of DR phases can assist clinicians in deciding on appropriate intervention techniques. Diabetic patients all over the world require regular screening to aid in early detection and treatment delivery. Nearly 90% of diabetes individuals canbedetected with early illness detection and adequate screening, and disease development can be slowed by avoiding future repercussions. The main issue is that DR does not reveal characteristic symptoms until the disease has progressed to an advanced stage [3]. To avoid difficulties, periodic eye examinations and regularcheck-upsareencouraged.Human evaluation of retinal characteristics and morphological differences in fundus images, on the other hand, is a tedious and time-consumingoperation.Toaddressthis shortcoming, numerous automated computer-aided diagnostic toolshave recently been developed, which assist ophthalmologists in examining retinal abnormalities. 2. RELATED WORK Researchers have devised or applied effective techniques for diagnosing diabetic retinopathy in two ways: binary classification and multi classification, as shown below. Several techniques for detecting microaneurysms, hemorrhages, and exudates are discussed [1] for ultimate detection of non-proliferative diabetic retinopathy. Blood vessels detection techniques are also discussed for the diagnosis of proliferative diabetic retinopathy. A number of image processing techniquesapplicable to whitelightretinal fundus images have been proposed in the literature [2], which were used to design screening systems for this retinal disorder. A common prerequisite step used in all the approaches is the blood vessel network extraction. Based on the retinal image processing techniques used, the screening systemscan be furthercategorizedasthosewhichareusedto design DR referral systemsfocusingonlocalizationofasingle symptom and those DR referralsystemsfocusingonisolation of multiple symptoms. Various conventional and deep learning-based diabetic retinopathy disease detection and classification methods are reviewed [3] and analyzed to provide a clear insight and future directions. Meher Madhu Dharmana et.al. [4] proposed method which has an effective feature extraction technique based on blob detection followed by classification of different stages of diabetic retinopathy using machine learning technique. This feature extractiontechniquecouldhelpautomaticcharacterizationof retina images fordiabetic retinopathy withan accuracyof83 per cent with the most efficient machine learning classification algorithm, which would help specialists to handily recognize the patient’s condition in a progressively precise manner. Messadi Mohamed et.al. [5] presented approach is based on the segmentation of blood vessels and extracts the geometric features, which are used in the early detection of diabetic retinopathy. The proposed system was tested on the DRIVE and Messidordatabasesandachievedan average sensitivity,specificityandaccuracyof89%,99%and 96%, respectively forthe segmentation of retinalvesselsand 91%, 100% and 93%, respectively for the classification of diabetic retinopathy. Doshna Umma Reddy et.al. [6] considered a convolutional neural network which uses the VGG- 16 model as a pre-trained neural network for fine- tuning,and, thereby classifying the severity ofDR.Themodel also uses efficient deep learning techniques including data augmentation,batchnormalization,dropoutlayersandlearn- rate scheduling on high resolution images to achieve higher levels of accuracy. J. Anitha et.al. [7] developedCAD techniquesareanalyzed with respect to performance evaluation and the challenges are discussed, some suitable solutions are suggested for improving the system to be more accurate. R. Subhashini et.al. [8] constructed a graphical user interface that can integrate image processing techniques together in order to predict whether the input fundus/retinal image received
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International Research Journal
of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 09 Issue: 08 | Aug 2022 www.irjet.net p-ISSN: 2395-0072 © 2022, IRJET | Impact Factor value: 7.529 | ISO 9001:2008 Certified Journal | Page 242 from the patient isaffected with DiabeticRetinopathy ornot; if affected, the graphical user interface will display the severity along with the required action needed to be undertaken by the user / patient. Manoj Kumar Behera et.al. [9] has proposed research two well-known predefined feature extraction techniques scale invariant feature transform (SIFT) and speeded up robust features (SURF) have been used simultaneously on each retinal images to capture the Exudates regions. These Exudates of each image stored in a feature matrix and used by the support vector machine (SVM) classifier for prediction of DR. Karan Bhatia et.al. [10] focused on decision about the presence of disease by applying ensemble of machine learning classifying algorithms on features extracted from output of different retinal image processing algorithms, like diameter of optic disk, lesion specific (microaneurysms,exudates),imagelevel (prescreening,AM/FM,qualityassessment).Decisionmaking for predicting the presence of diabetic retinopathy was performed using alternating decision tree, Ada-Boost, Naïve Bayes, Random Forest and SVM. Masoud Khazaee Fadafen et.al. [11] proposed method on the DIARETDB1 database, which includes 89 selected images for the diagnosis of diabetic retinopathy, was tested and with four models of methodsavailableforrecognizingsaliencies,frequencytuned method (FT) model, the spectral residual approach (SR) model, the SDSP model:a novel saliency detectionmethodby combining simple prior has been compared. To evaluate the performance of the proposed method with other methods using Ground truth images, the ROC curve and the AUC calculation were used. Sumesh E P et.al. [12] created a DR detection technique, involving digital image processing, has been developed by utilizing retinal image, where fundus image has been obtained from patient’sretina.Thisproposed work aims at segmenting the fundus image into Exudates, Micro aneurysm, Optical Disk and hemorrhage and examine whether the retinal condition is in Proliferative / Non Proliferative DR stage. Various performance measures has been utilized in validating the proposed technique. From those performance analysis, wecould observe 98%accuracy in detecting PDR and NPDR within 39 seconds (half minute). Ali Shojaeipour et.al. [13] developed system in which the Gaussian filter is used to enhance images and separate vessels with a high brightness intensity distribution. Next, wavelets transform is used to extract vessels. After that according to some criteria such as vessels density, the location of optic disc was determined. Then after optic disc extraction, exudates regions were determined. Finally they classified the images with a boosting classifier. With utilizing the boosting algorithm, the suggested system can have a power classifier. Mirthula Balaji et.al. [14] implemented a semantic analysis that utilizes for portraying the DR. In our proposed methodology, an innovative framework to overcome the issues of traditional methodology. The GLCM an effective feature is chosen for extracting the features with the co- occurrence matrix. After extracting the features, the classification process is performedusingProbabilisticNeural Network(PNN) which provides an effective classifieroutput. It is concluded that this novel vessel segmentation frameworkacquired better accuracy,sensitivity,Fmeasures, specificityand precision from thisexperiment.YuhanisYusof et.al. [15] focuses on classification of fundus image that contains with or without signs of DR and utilizes artificial neuralnetwork(NN)namelyMulti-layeredPerceptron(MLP) trained by Levenberg-Marquardt (LM) and Bayesian Regularization (BR) to classify the data. Nineteen features have been extracted from fundus image and used as neural network inputs for the classification. It is learned that MLP trained with BR provides a better classification performance with 72.11% (training) and 67.47%(testing)as comparedto the use of LM. Shailesh Kumar et.al. [16] presents an improved diabetic retinopathy detection scheme by extracting accurate area and ate number of micro aneurysm from color fundus images.Diabeticretinopathy(DR)isaneye disease which occurs due to damage of retina as a result of long illness of diabetic mellitus. The recognition of MA at primary stage is very crucial and it is the first step in inhibiting DR. A variety of methods have been proposed for detection and diagnosis of DR. Classification of DR has been done by linear Supportvectormachine(SVM).Thesensitivity and specificity of DR detection system are observed as 96% and 92% respectively. Bhavani Sambaturu et.al. [17] proposed a novel method to detect hard exudates with high accuracy with respect to lesion level. They tested our algorithm on publicly available DiaretDB database, which contains the ground truth for all images. They achieved high performance results such as sensitivityof0.87andF-Scoreof 0.78 and Positive Predict Value (PPV) of 0.76 for hard exudatelesionlevel detection, compared to the existingstate of art techniques. Tanapat Ratanapakorn et.al. [18] has the automated software for screening and diagnosing DR, by using the combinationofdigitalimageprocessingtechniques, has been developed. This software yields the good accuracy for the detection of DR from fundus photographs. It can be used as an alternative or adjunctive tool for DR screening, especially in the remote area where ophthalmologist is not available or in the rural area where ophthalmologist has many task overloads. 3. CONCLUSIONS Although diabetic retinopathy cannot be healed, laser analysis can help prevent visionlossifdonebeforetheretina is negatively affected. The surgical removal of vitreous gel can enhance eyesight if the retina has not been severely damaged. This research aids in the early diagnosis of retinopathy, which can lead to irreversible visual loss if not treated promptly. This studydetailedtheauthors'studiesfor detecting diabetic retinopathy. Technical people and researchers who need to leverage ongoing research in this field would benefit from our effort. Various approaches for detecting and treating diabetic retinopathy patients have been developed, including the categorization of different
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International Research Journal
of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 09 Issue: 08 | Aug 2022 www.irjet.net p-ISSN: 2395-0072 © 2022, IRJET | Impact Factor value: 7.529 | ISO 9001:2008 Certified Journal | Page 243 phases of diabetic retinopathy employing using artificial intelligence. REFERENCES [1] Javeria Amin, Muhammad Sharif, Mussarat Yasmin, "A Review on Recent Developments for Detection of Diabetic Retinopathy", Scientifica, vol. 2016, Article ID 6838976, 20 pages, 2016. https://doi.org/10.1155/2016/6838976 [2] Sandhya Soman, Jayashree R, “A Survey of Image Processing Techniques for Diabetic Retinopathy”, International Conference on Advances in computer Science and Technology(IC-ACT’18) – 2018,ISSN:2395- 1303 [3] Valarmathi S, Dr. R. Vijayabhanu, “A Review on Diabetic Retinopathy Disease Detection and Classification using Image Processing Techniques”, International Research Journal of Engineering and Technology(IRJET),Volume: 07 Issue: 09 | Sep 2020, pp 456-455. [4] M. M. Dharmana and A. M.S., "Pre-diagnosis of Diabetic Retinopathy using Blob Detection," 2020 Second International Conference on Inventive Research in Computing Applications (ICIRCA), Coimbatore, India, 2020, pp. 98-101, doi: 10.1109/ICIRCA48905.2020.9183241. [5] E. Z. Aziza, L. Mohamed El Amine, M. Mohamed and B. Abdelhafid, "Decision tree CART algorithm for diabetic retinopathy classification," 2019 6th International Conference on Image and Signal Processing and their Applications (ISPA), Mostaganem,Algeria,2019,pp.1-5, doi: 10.1109/ISPA48434.2019.8966905. [6] N. B. Thota and D. Umma Reddy, "Improving the Accuracy of Diabetic Retinopathy SeverityClassification with Transfer Learning," 2020 IEEE 63rd International Midwest Symposium on Circuits and Systems (MWSCAS), Springfield, MA, USA, 2020, pp. 1003-1006, doi: 10.1109/MWSCAS48704.2020.9184473. [7] A. G. P. H., J. Anitha and J. N. R. J., "Computer Aided Diagnosis Methods for Classification of Diabetic Retinopathy Using Fundus Images," 2018 International Conference on Circuits and SystemsinDigital Enterprise Technology (ICCSDET), Kottayam, India, 2018, pp. 1-4, doi: 10.1109/ICCSDET.2018.8821200. [8] R. Subhashini, T. N. R. Nithin, U. M. S. Koushik, “Diabetic Retinopathy Detection using Image Processing (GUI)”, International Journal of Recent Technology and Engineering (IJRTE), ISSN: 2277-3878,Volume-8,Issue- 2S3, July 2019 [9] M. K. Behera and S. Chakravarty, "Diabetic Retinopathy Image Classification Using Support Vector Machine," 2020 International Conference on Computer Science, Engineering and Applications (ICCSEA), Gunupur,India, 2020, pp. 1-4, doi: 10.1109/ICCSEA49143.2020.9132875. [10] K. Bhatia, S. Arora and R. Tomar, "Diagnosis of diabetic retinopathy using machine learning classification algorithm," 2016 2nd International Conference on Next Generation Computing Technologies(NGCT),Dehradun, 2016, pp. 347-351, doi: 10.1109/NGCT.2016.7877439. [11] Fadafen, Masoud & Mehrshad, Nasser & Razavi, Seyyed. (2018). Detection of diabetic retinopathy using computational model of human visual system. Biomedical Research (India). 29. 1956-1960. 10.4066/biomedicalresearch.29-18-551. [12] Hamood Ali Hamood Al shamaly, Sumesh E P, Vidhyalavanya R, Jayakumari C, “Diabetic Retinopathy Detection Using Matlab”, International Journal Of Scientific & Technology Research Volume 8, Issue 11, November 2019 [13] A. Shojaeipour, M. J. Nordin and N. Hadavi, "Using image processing methods for diagnosis diabeticretinopathy," 2014 IEEE International Symposium on Robotics and Manufacturing Automation (ROMA), Kuala Lumpur, 2014, pp. 154-159, doi: 10.1109/ROMA.2014.7295879. [14] V. Govindaraj, M. Balaji, T. a. Mohideen and S. a. F. J. mohideen, "Eminent identification and classification of Diabetic Retinopathy in clinical fundus images using Probabilistic Neural Network," 2019 IEEE International Conference on Intelligent Techniques in Control, Optimization and Signal Processing(INCOS),Tamilnadu, India, 2019, pp. 1-6, doi: 10.1109/INCOS45849.2019.8951349. [15] N. H. Harun, Y. Yusof, F. Hassan and Z. Embong, "Classification of Fundus Images For Diabetic Retinopathy usingArtificial Neural Network,"2019IEEE Jordan International Joint Conference on Electrical Engineering and Information Technology (JEEIT), Amman, Jordan, 2019, pp. 498-501, doi: 10.1109/JEEIT.2019.8717479. [16] S. Kumar and B. Kumar, "DiabeticRetinopathyDetection by Extracting Area and Number of Microaneurysmfrom Colour Fundus Image," 2018 5th International Conference on Signal Processing and Integrated Networks (SPIN), Noida, 2018, pp. 359-364, doi: 10.1109/SPIN.2018.8474264. [17] K. K. Palavalasa and B. Sambaturu, "Automatic Diabetic Retinopathy Detection Using Digital Image Processing," 2018 International Conference on Communication and
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International Research Journal
of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 09 Issue: 08 | Aug 2022 www.irjet.net p-ISSN: 2395-0072 © 2022, IRJET | Impact Factor value: 7.529 | ISO 9001:2008 Certified Journal | Page 244 Signal Processing (ICCSP), Chennai, 2018, pp. 0072- 0076, doi: 10.1109/ICCSP.2018.8524234. [18] Ratanapakorn, Tanapat & Daengphoonphol,Athiwath& Eua-Anant, Nawapak & Yospaiboon, Yosanan. (2019). Digital image processing software for diagnosing diabetic retinopathy from fundus photograph. Clinical Ophthalmology. Volume 13. 641-648. 10.2147/OPTH.S195617.
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