Detection of Diabetic Retinopathy using Kirsch Edge Detection and Watershed T...IJARIIT
Diabetic retinopathy (DR) is a common retinal complication associated with diabetics. A complication of diabetes is that it can also affect various parts of the body. When the small blood vessels have a high level of glucose in the retina, the vision will be blurred and can cause blindness eventually, which is known as diabetic retinopathy. However, if symptoms are identified in the early stage then proper treatment can be provided to prevent blindness. Usually the retinal images obtained from the fundus camera are examined directly and diagnosed. Due to this certain abnormalities due to diabetic retinopathy are not directly visible through the naked eye .Hence by using the image processing techniques these abnormalities can be extracted accurately and required treatments and precautions can be taken. And this also reduces the time for the ophthalmologists to detect the disease and give accurate treatments.
Diabetic Retinopathy Detection System from Retinal Imagesijtsrd
Diabetes Mellitus is a disorder in metabolism of carbohydrates, and due to lack of the pancreatic hormone insulin sugars in the body are not oxidized to produce energy. Diabetic Retinopathy is a disorder of the retina resulting in impairment or vision loss. Improper blood sugar control is the main cause of diabetic retinopathy. That is the reason why early detection of retinopathy is crucial to prevent vision loss. Appearance of exudates, microaneurysms and hemorrhages are the early indications. In this study, we propose an algorithm for detection and classification of diabetic retinopathy. The proposed algorithm is based on the combination of various image processing techniques, which includes Contrast Limited Adaptive Histogram Equalization, Green channelization, Filtering and Thresholding. The objective measurements such as homogeneity, entropy, contrast, energy, dissimilarity, asm, correlation, mean and standard deviation are computed from processed images. These measurements are finally fed to Support Vector Machine and k Nearest Neighbors classifiers for classification and their results were analysed and compared. Aditi Devanand Lotliker | Amit Patil "Diabetic Retinopathy Detection System from Retinal Images" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-2 , February 2021, URL: https://www.ijtsrd.com/papers/ijtsrd38353.pdf Paper Url: https://www.ijtsrd.com/engineering/computer-engineering/38353/diabetic-retinopathy-detection-system-from-retinal-images/aditi-devanand-lotliker
An Amalgamation-Based System for Micro aneurysm Detection and Diabetic Retino...IJMER
We propose an ensemble-based framework to improve microaneurysm detection. Unlike
the well-known approach of considering the output of multiple classifiers, we propose a combination of
internal components of microaneurysm detectors, namely preprocessing methods and candidate
extractors. We have evaluated our approach for microaneurysm detection in an online competition,
where this algorithm is currently ranked as first, and also on two other databases.
This research detects the presence of abnormalities in the retina using image
processing techniques by applying morphological processing to the fundus
images to extract features such as blood vessels, micro aneurysms,
haemorrhages ,exudates and neo vascularization.
Automated Detection of Microaneurysm, Hard Exudates, and Cotton Wool Spots in...iosrjce
The The automatic identification of Image processing techniques for abnormalities in retinal images.
Its very importance in diabetic retinopathy screening. Manual annotations of retinal images are rare and
exclusive to obtain. The ophthalmoscope used direct analysis is a small and portable apparatus contained of a
light source and a set of lenses view the retina. The existence of diabetic retinopathy detected can be examining
the retina for its individual features. The first presence of diabetic retinopathy is the form of Microaneurysms.
This paper describes different works needed to the automatic identification of hard exudates and cotton wool
spots in retinal images for diabetic retinopathy detection and support vector machine (SVM) for classifying
images. This system is evaluated on a large dataset containing 130 retinal images. The proposed method Results
show that exudates were detected from a database with 96.9% sensitivity, specificity 96.1% and
97.38%accuracy
Detection of Diabetic Retinopathy using Kirsch Edge Detection and Watershed T...IJARIIT
Diabetic retinopathy (DR) is a common retinal complication associated with diabetics. A complication of diabetes is that it can also affect various parts of the body. When the small blood vessels have a high level of glucose in the retina, the vision will be blurred and can cause blindness eventually, which is known as diabetic retinopathy. However, if symptoms are identified in the early stage then proper treatment can be provided to prevent blindness. Usually the retinal images obtained from the fundus camera are examined directly and diagnosed. Due to this certain abnormalities due to diabetic retinopathy are not directly visible through the naked eye .Hence by using the image processing techniques these abnormalities can be extracted accurately and required treatments and precautions can be taken. And this also reduces the time for the ophthalmologists to detect the disease and give accurate treatments.
Diabetic Retinopathy Detection System from Retinal Imagesijtsrd
Diabetes Mellitus is a disorder in metabolism of carbohydrates, and due to lack of the pancreatic hormone insulin sugars in the body are not oxidized to produce energy. Diabetic Retinopathy is a disorder of the retina resulting in impairment or vision loss. Improper blood sugar control is the main cause of diabetic retinopathy. That is the reason why early detection of retinopathy is crucial to prevent vision loss. Appearance of exudates, microaneurysms and hemorrhages are the early indications. In this study, we propose an algorithm for detection and classification of diabetic retinopathy. The proposed algorithm is based on the combination of various image processing techniques, which includes Contrast Limited Adaptive Histogram Equalization, Green channelization, Filtering and Thresholding. The objective measurements such as homogeneity, entropy, contrast, energy, dissimilarity, asm, correlation, mean and standard deviation are computed from processed images. These measurements are finally fed to Support Vector Machine and k Nearest Neighbors classifiers for classification and their results were analysed and compared. Aditi Devanand Lotliker | Amit Patil "Diabetic Retinopathy Detection System from Retinal Images" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-2 , February 2021, URL: https://www.ijtsrd.com/papers/ijtsrd38353.pdf Paper Url: https://www.ijtsrd.com/engineering/computer-engineering/38353/diabetic-retinopathy-detection-system-from-retinal-images/aditi-devanand-lotliker
An Amalgamation-Based System for Micro aneurysm Detection and Diabetic Retino...IJMER
We propose an ensemble-based framework to improve microaneurysm detection. Unlike
the well-known approach of considering the output of multiple classifiers, we propose a combination of
internal components of microaneurysm detectors, namely preprocessing methods and candidate
extractors. We have evaluated our approach for microaneurysm detection in an online competition,
where this algorithm is currently ranked as first, and also on two other databases.
This research detects the presence of abnormalities in the retina using image
processing techniques by applying morphological processing to the fundus
images to extract features such as blood vessels, micro aneurysms,
haemorrhages ,exudates and neo vascularization.
Automated Detection of Microaneurysm, Hard Exudates, and Cotton Wool Spots in...iosrjce
The The automatic identification of Image processing techniques for abnormalities in retinal images.
Its very importance in diabetic retinopathy screening. Manual annotations of retinal images are rare and
exclusive to obtain. The ophthalmoscope used direct analysis is a small and portable apparatus contained of a
light source and a set of lenses view the retina. The existence of diabetic retinopathy detected can be examining
the retina for its individual features. The first presence of diabetic retinopathy is the form of Microaneurysms.
This paper describes different works needed to the automatic identification of hard exudates and cotton wool
spots in retinal images for diabetic retinopathy detection and support vector machine (SVM) for classifying
images. This system is evaluated on a large dataset containing 130 retinal images. The proposed method Results
show that exudates were detected from a database with 96.9% sensitivity, specificity 96.1% and
97.38%accuracy
A Novel Approach for Diabetic Retinopthy ClassificationIJERA Editor
Sustainable Diabetic Mellitus may lead to several complications towards patients. One of the complications is
diabetic retinopathy. Diabetic retinopathy is the type of complication towards the retinal and interferes with
patient’s sight. Medical examination toward patients with diabetic retinopathy is observed directly through
retinal images using fundus camera. Diabetic retinopathy is classified into four classes based on severity, which
are: normal, non-proliferative diabetic retinopathy (NPDR), proliferative diabetic retinopathy (PDR), and
macular edema (ME). The aim of this research is to develop a method which can be used to classify the level of
severity of diabetic retinopathy based on patient’s retinal images. Seven texture features were extracted from
retinal images using gray level co-occurence matrix three dimensional method (3D-GLCM). These features are
maximum probability, correlation, contrast, energy, homogeneity, and entropy; subsequently trained using
Levenberg-Marquardt Backpropagation Neural Network (LMBP). This study used 600 data of patient’s retinal
images, consist of 450 data retinal images for training and 150 data retinal images for testing. Based on the result
of this test, the method can classify the severity of diabetic retinopathy with sensitivity of 97.37%, specificity of
75% and accuracy of 91.67%
Diabetic Retinopathy Detection using Neural Networkingijtsrd
The clinical and laboratory studies states that diabetic retinopathy is the major cause of permanent blindness among the aged personalities. The problem with this disease is that there is no cure for it and the only thing we can do is to detect the disease as soon as possible in order to prevent further loss of vision. In this system we propose a CNN approach for diagnosing DR from retinal images and classifying the stages of the disease .The classification is done based on the haemorrhages, micro aneurysms present in the retinal image. We train this network using a high end graphics processor unit GPU using kaggle data set and the disease classification is done, hence we can identify the the disease and prevent the further loss of vision. N Rahul | Roy Eluvathingal | Sanith Jayan K | Mr. Anil Antony "Diabetic Retinopathy Detection using Neural Networking" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-4 , June 2020, URL: https://www.ijtsrd.com/papers/ijtsrd31487.pdf Paper Url :https://www.ijtsrd.com/engineering/computer-engineering/31487/diabetic-retinopathy-detection-using-neural-networking/n-rahul
C LASSIFICATION O F D IABETES R ETINA I MAGES U SING B LOOD V ESSEL A REASIJCI JOURNAL
Retina images are obtained from the fundus camera a
nd graded by skilled professionals. However there i
s
considerable shortage of expert observers has encou
raged computer assisted monitoring. Evaluation of
blood vessels network plays an important task in a
variety of medical diagnosis. Manifestations of
numerous vascular disorders, such as diabetic retin
opathy, depend on detection of the blood vessels
network. In this work the fundus RGB image is used
for obtaining the traces of blood vessels and areas
of
blood vessels are used for detection of Diabetic Re
tinopathy (DR). The algorithm developed uses
morphological operation to extract blood vessels. M
ainly two steps are used: firstly enhancement opera
tion
is applied to original retina image to remove noise
and increase contrast of retinal blood vessels. Se
condly
morphology operations are used to take out blood ve
ssels. Experiments are conducted on publicly availa
ble
DIARETDB1 database. Experimental results obtained b
y using gray-scale images have been presented.
An Automated Systems for the Detection of Macular Ischemia based-Diabetic Ret...iosrjce
The proposed methodology in this paper marks out application for automatic detection of eye
diseases called Macular Ischemia using image processing techniques. In semi urban and rural areas large
percentages of people suffer from various eye diseases. For diagnoses of various eye diseases, Image processing
technique is used. . Diseases occur in Macula from retinal images have a huge type of textures, shapes and at
times they are difficult to be recognised and identified by doctors. Thus we are trying to optimize and develop
such system which is based on smart image recognition/classification algorithms. This proposed system
provides accuracy, uniformity and speed in performance and a high credence coefficient in results interpreting.
Keywords: Macular Ischemia, diagnosis, textures, consistence
In this case-based presentation, Dr. Lori Myers unscrambles the alphabet soup of Diabetic Retinopathy, providing clear explanations and outstanding images to describe the diagnosis, risk stratification, and treatment of diabetic retinopathy.
The legal cause of blindness for the workingage
population in western countries is Diabetic Retinopathy - a
complication of diabetes mellitus - is a severe and wide- spread
eye disease. Digital color fundus images are becoming
increasingly important for the diagnosis of Diabetic Retinopathy.
In order to facilitate and improve diagnosis in different ways, this
fact opens the possibility of applying image processing techniques
.Microaneurysms is the earliest sign of DR, therefore an
algorithm able to automatically detect the microaneurysms in
fundus image captured. Since microaneurysms is a necessary
preprocessing step for a correct diagnosis. Some methods that
address this problem can be found in the literature but they have
some drawbacks like accuracy or speed. The aim of this thesis is
to develop and test a new method for detecting the
microaneurysms in retina images. To do so preprocessing, gray
level 2D feature based vessel extraction is done using neural
network by using extra neurons which is evaluated on DRIVE
database which is superior than rulebased methods. To identify
microaneurysms in an image morphological opening and image
enhancement is performed. The complete algorithm is developed
by using a MATLAB implementation and the diagnosis in an
image can be estimated with the better accuracy and in shorter
time than previous techniques
EXUDATES DETECTION FROM DIGITAL FUNDUS IMAGE OF DIABETIC RETINOPATHYijabjournal
The aim of this paper is to detect exudates from the digital fundus images and provide information about Non Proliferative Diabetic Retinopathy. Diabetic retinopathy is very complicated disease that occurs when the retinal blood vessels changes. Exudates are the first sign of the diabetic retinopathy which cause blindness. So it is very important to find out these exudates in fundus image. In this paper we have proposed a method which is used for segmentation of optic disc and exudates. Morphological operations are used for detection of exudates. Before this operation we are applying Contrast Limited Adaptive Histogram Equalization technique. The results are compared with the standard database.
Diabetic retinopathy is a disease, caused by alternation in the retinal blood vessels. It is a strong sign of early blindness and if it is not treated may tend to complete blindness and the vision lost once cannot be restored once again. In this paper different image processing techniques are used to differentiate between the normal and the diseased image. The attempt is made to see where the problem actually lies so that proper diagnosis of patient can be done. Pre processing of an image, optic disk detection, Blood vessels extraction, Exudates detection are some of the methods that are applied here. Other algorithms are designed to obtain the desired result. A large number of populations are affected by this disease around the world.
A Novel Advanced Approach Using Morphological Image Processing Technique for ...CSCJournals
Diabetic retinopathy (DR) is a common complication of diabetes mellitus and can lead to irreversible blindness. To date, DR is the leading cause of blindness and visual impairment among working adults globally. However, this blindness can be prevented if DR is detected early. Diabetes mellitus slowly affects the retina by damaging retinal blood vessels and leading to microaneurysms. The retinal images give detailed information about the health status of the visual system. Analysis of retinal image is important for an understanding of the stages of Diabetic retinopathy. Microaneurysms observed that appear in retina images, usually, the initial visible sign of DR, if detected early and properly treated can prevent DR complications, including blindness. In this research work, an advanced image modal enhancement comprises of a Contrast Limited Adaptive Histogram Equalization (CLAHE), through morphological image, processing technique with final extraction algorithm is proposed. CLAHE is responsible for the detection, and removal of the retinal optical disk. While the microaneurysm initial indicators are detected by using morphological image processing techniques. The extensive evaluation of the proposed advanced model conducted for microaneurysm detection depicts all stages of DR with an increase in the number of data set related to noise in the image. The microaneurysms noise is associated with stage of retina diseases as well as its early possible diagnosis. Evaluation is also conducted against the proposed model to measure its performance in terms of accuracy, sensitivity as well as specificity in real-time. The results show the test image attained 99.7% accuracy for a real-time database that is better compared with anty colony-based method. A sensitivity of 81% with a specificity of 90% was achieved for the detection of microaneurysms for the e-optha database. The detection of several microaneurysms correlates with stages of DR that prove an analysis of detecting its different stages. As well as it reaches our goal of early detection of DR with high performance in accuracy.
Introduction to AI for Nonprofits with Tapp NetworkTechSoup
Dive into the world of AI! Experts Jon Hill and Tareq Monaur will guide you through AI's role in enhancing nonprofit websites and basic marketing strategies, making it easy to understand and apply.
Embracing GenAI - A Strategic ImperativePeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
A Novel Approach for Diabetic Retinopthy ClassificationIJERA Editor
Sustainable Diabetic Mellitus may lead to several complications towards patients. One of the complications is
diabetic retinopathy. Diabetic retinopathy is the type of complication towards the retinal and interferes with
patient’s sight. Medical examination toward patients with diabetic retinopathy is observed directly through
retinal images using fundus camera. Diabetic retinopathy is classified into four classes based on severity, which
are: normal, non-proliferative diabetic retinopathy (NPDR), proliferative diabetic retinopathy (PDR), and
macular edema (ME). The aim of this research is to develop a method which can be used to classify the level of
severity of diabetic retinopathy based on patient’s retinal images. Seven texture features were extracted from
retinal images using gray level co-occurence matrix three dimensional method (3D-GLCM). These features are
maximum probability, correlation, contrast, energy, homogeneity, and entropy; subsequently trained using
Levenberg-Marquardt Backpropagation Neural Network (LMBP). This study used 600 data of patient’s retinal
images, consist of 450 data retinal images for training and 150 data retinal images for testing. Based on the result
of this test, the method can classify the severity of diabetic retinopathy with sensitivity of 97.37%, specificity of
75% and accuracy of 91.67%
Diabetic Retinopathy Detection using Neural Networkingijtsrd
The clinical and laboratory studies states that diabetic retinopathy is the major cause of permanent blindness among the aged personalities. The problem with this disease is that there is no cure for it and the only thing we can do is to detect the disease as soon as possible in order to prevent further loss of vision. In this system we propose a CNN approach for diagnosing DR from retinal images and classifying the stages of the disease .The classification is done based on the haemorrhages, micro aneurysms present in the retinal image. We train this network using a high end graphics processor unit GPU using kaggle data set and the disease classification is done, hence we can identify the the disease and prevent the further loss of vision. N Rahul | Roy Eluvathingal | Sanith Jayan K | Mr. Anil Antony "Diabetic Retinopathy Detection using Neural Networking" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-4 , June 2020, URL: https://www.ijtsrd.com/papers/ijtsrd31487.pdf Paper Url :https://www.ijtsrd.com/engineering/computer-engineering/31487/diabetic-retinopathy-detection-using-neural-networking/n-rahul
C LASSIFICATION O F D IABETES R ETINA I MAGES U SING B LOOD V ESSEL A REASIJCI JOURNAL
Retina images are obtained from the fundus camera a
nd graded by skilled professionals. However there i
s
considerable shortage of expert observers has encou
raged computer assisted monitoring. Evaluation of
blood vessels network plays an important task in a
variety of medical diagnosis. Manifestations of
numerous vascular disorders, such as diabetic retin
opathy, depend on detection of the blood vessels
network. In this work the fundus RGB image is used
for obtaining the traces of blood vessels and areas
of
blood vessels are used for detection of Diabetic Re
tinopathy (DR). The algorithm developed uses
morphological operation to extract blood vessels. M
ainly two steps are used: firstly enhancement opera
tion
is applied to original retina image to remove noise
and increase contrast of retinal blood vessels. Se
condly
morphology operations are used to take out blood ve
ssels. Experiments are conducted on publicly availa
ble
DIARETDB1 database. Experimental results obtained b
y using gray-scale images have been presented.
An Automated Systems for the Detection of Macular Ischemia based-Diabetic Ret...iosrjce
The proposed methodology in this paper marks out application for automatic detection of eye
diseases called Macular Ischemia using image processing techniques. In semi urban and rural areas large
percentages of people suffer from various eye diseases. For diagnoses of various eye diseases, Image processing
technique is used. . Diseases occur in Macula from retinal images have a huge type of textures, shapes and at
times they are difficult to be recognised and identified by doctors. Thus we are trying to optimize and develop
such system which is based on smart image recognition/classification algorithms. This proposed system
provides accuracy, uniformity and speed in performance and a high credence coefficient in results interpreting.
Keywords: Macular Ischemia, diagnosis, textures, consistence
In this case-based presentation, Dr. Lori Myers unscrambles the alphabet soup of Diabetic Retinopathy, providing clear explanations and outstanding images to describe the diagnosis, risk stratification, and treatment of diabetic retinopathy.
The legal cause of blindness for the workingage
population in western countries is Diabetic Retinopathy - a
complication of diabetes mellitus - is a severe and wide- spread
eye disease. Digital color fundus images are becoming
increasingly important for the diagnosis of Diabetic Retinopathy.
In order to facilitate and improve diagnosis in different ways, this
fact opens the possibility of applying image processing techniques
.Microaneurysms is the earliest sign of DR, therefore an
algorithm able to automatically detect the microaneurysms in
fundus image captured. Since microaneurysms is a necessary
preprocessing step for a correct diagnosis. Some methods that
address this problem can be found in the literature but they have
some drawbacks like accuracy or speed. The aim of this thesis is
to develop and test a new method for detecting the
microaneurysms in retina images. To do so preprocessing, gray
level 2D feature based vessel extraction is done using neural
network by using extra neurons which is evaluated on DRIVE
database which is superior than rulebased methods. To identify
microaneurysms in an image morphological opening and image
enhancement is performed. The complete algorithm is developed
by using a MATLAB implementation and the diagnosis in an
image can be estimated with the better accuracy and in shorter
time than previous techniques
EXUDATES DETECTION FROM DIGITAL FUNDUS IMAGE OF DIABETIC RETINOPATHYijabjournal
The aim of this paper is to detect exudates from the digital fundus images and provide information about Non Proliferative Diabetic Retinopathy. Diabetic retinopathy is very complicated disease that occurs when the retinal blood vessels changes. Exudates are the first sign of the diabetic retinopathy which cause blindness. So it is very important to find out these exudates in fundus image. In this paper we have proposed a method which is used for segmentation of optic disc and exudates. Morphological operations are used for detection of exudates. Before this operation we are applying Contrast Limited Adaptive Histogram Equalization technique. The results are compared with the standard database.
Diabetic retinopathy is a disease, caused by alternation in the retinal blood vessels. It is a strong sign of early blindness and if it is not treated may tend to complete blindness and the vision lost once cannot be restored once again. In this paper different image processing techniques are used to differentiate between the normal and the diseased image. The attempt is made to see where the problem actually lies so that proper diagnosis of patient can be done. Pre processing of an image, optic disk detection, Blood vessels extraction, Exudates detection are some of the methods that are applied here. Other algorithms are designed to obtain the desired result. A large number of populations are affected by this disease around the world.
A Novel Advanced Approach Using Morphological Image Processing Technique for ...CSCJournals
Diabetic retinopathy (DR) is a common complication of diabetes mellitus and can lead to irreversible blindness. To date, DR is the leading cause of blindness and visual impairment among working adults globally. However, this blindness can be prevented if DR is detected early. Diabetes mellitus slowly affects the retina by damaging retinal blood vessels and leading to microaneurysms. The retinal images give detailed information about the health status of the visual system. Analysis of retinal image is important for an understanding of the stages of Diabetic retinopathy. Microaneurysms observed that appear in retina images, usually, the initial visible sign of DR, if detected early and properly treated can prevent DR complications, including blindness. In this research work, an advanced image modal enhancement comprises of a Contrast Limited Adaptive Histogram Equalization (CLAHE), through morphological image, processing technique with final extraction algorithm is proposed. CLAHE is responsible for the detection, and removal of the retinal optical disk. While the microaneurysm initial indicators are detected by using morphological image processing techniques. The extensive evaluation of the proposed advanced model conducted for microaneurysm detection depicts all stages of DR with an increase in the number of data set related to noise in the image. The microaneurysms noise is associated with stage of retina diseases as well as its early possible diagnosis. Evaluation is also conducted against the proposed model to measure its performance in terms of accuracy, sensitivity as well as specificity in real-time. The results show the test image attained 99.7% accuracy for a real-time database that is better compared with anty colony-based method. A sensitivity of 81% with a specificity of 90% was achieved for the detection of microaneurysms for the e-optha database. The detection of several microaneurysms correlates with stages of DR that prove an analysis of detecting its different stages. As well as it reaches our goal of early detection of DR with high performance in accuracy.
Introduction to AI for Nonprofits with Tapp NetworkTechSoup
Dive into the world of AI! Experts Jon Hill and Tareq Monaur will guide you through AI's role in enhancing nonprofit websites and basic marketing strategies, making it easy to understand and apply.
Embracing GenAI - A Strategic ImperativePeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
June 3, 2024 Anti-Semitism Letter Sent to MIT President Kornbluth and MIT Cor...Levi Shapiro
Letter from the Congress of the United States regarding Anti-Semitism sent June 3rd to MIT President Sally Kornbluth, MIT Corp Chair, Mark Gorenberg
Dear Dr. Kornbluth and Mr. Gorenberg,
The US House of Representatives is deeply concerned by ongoing and pervasive acts of antisemitic
harassment and intimidation at the Massachusetts Institute of Technology (MIT). Failing to act decisively to ensure a safe learning environment for all students would be a grave dereliction of your responsibilities as President of MIT and Chair of the MIT Corporation.
This Congress will not stand idly by and allow an environment hostile to Jewish students to persist. The House believes that your institution is in violation of Title VI of the Civil Rights Act, and the inability or
unwillingness to rectify this violation through action requires accountability.
Postsecondary education is a unique opportunity for students to learn and have their ideas and beliefs challenged. However, universities receiving hundreds of millions of federal funds annually have denied
students that opportunity and have been hijacked to become venues for the promotion of terrorism, antisemitic harassment and intimidation, unlawful encampments, and in some cases, assaults and riots.
The House of Representatives will not countenance the use of federal funds to indoctrinate students into hateful, antisemitic, anti-American supporters of terrorism. Investigations into campus antisemitism by the Committee on Education and the Workforce and the Committee on Ways and Means have been expanded into a Congress-wide probe across all relevant jurisdictions to address this national crisis. The undersigned Committees will conduct oversight into the use of federal funds at MIT and its learning environment under authorities granted to each Committee.
• The Committee on Education and the Workforce has been investigating your institution since December 7, 2023. The Committee has broad jurisdiction over postsecondary education, including its compliance with Title VI of the Civil Rights Act, campus safety concerns over disruptions to the learning environment, and the awarding of federal student aid under the Higher Education Act.
• The Committee on Oversight and Accountability is investigating the sources of funding and other support flowing to groups espousing pro-Hamas propaganda and engaged in antisemitic harassment and intimidation of students. The Committee on Oversight and Accountability is the principal oversight committee of the US House of Representatives and has broad authority to investigate “any matter” at “any time” under House Rule X.
• The Committee on Ways and Means has been investigating several universities since November 15, 2023, when the Committee held a hearing entitled From Ivory Towers to Dark Corners: Investigating the Nexus Between Antisemitism, Tax-Exempt Universities, and Terror Financing. The Committee followed the hearing with letters to those institutions on January 10, 202
Instructions for Submissions thorugh G- Classroom.pptxJheel Barad
This presentation provides a briefing on how to upload submissions and documents in Google Classroom. It was prepared as part of an orientation for new Sainik School in-service teacher trainees. As a training officer, my goal is to ensure that you are comfortable and proficient with this essential tool for managing assignments and fostering student engagement.
The Roman Empire A Historical Colossus.pdfkaushalkr1407
The Roman Empire, a vast and enduring power, stands as one of history's most remarkable civilizations, leaving an indelible imprint on the world. It emerged from the Roman Republic, transitioning into an imperial powerhouse under the leadership of Augustus Caesar in 27 BCE. This transformation marked the beginning of an era defined by unprecedented territorial expansion, architectural marvels, and profound cultural influence.
The empire's roots lie in the city of Rome, founded, according to legend, by Romulus in 753 BCE. Over centuries, Rome evolved from a small settlement to a formidable republic, characterized by a complex political system with elected officials and checks on power. However, internal strife, class conflicts, and military ambitions paved the way for the end of the Republic. Julius Caesar’s dictatorship and subsequent assassination in 44 BCE created a power vacuum, leading to a civil war. Octavian, later Augustus, emerged victorious, heralding the Roman Empire’s birth.
Under Augustus, the empire experienced the Pax Romana, a 200-year period of relative peace and stability. Augustus reformed the military, established efficient administrative systems, and initiated grand construction projects. The empire's borders expanded, encompassing territories from Britain to Egypt and from Spain to the Euphrates. Roman legions, renowned for their discipline and engineering prowess, secured and maintained these vast territories, building roads, fortifications, and cities that facilitated control and integration.
The Roman Empire’s society was hierarchical, with a rigid class system. At the top were the patricians, wealthy elites who held significant political power. Below them were the plebeians, free citizens with limited political influence, and the vast numbers of slaves who formed the backbone of the economy. The family unit was central, governed by the paterfamilias, the male head who held absolute authority.
Culturally, the Romans were eclectic, absorbing and adapting elements from the civilizations they encountered, particularly the Greeks. Roman art, literature, and philosophy reflected this synthesis, creating a rich cultural tapestry. Latin, the Roman language, became the lingua franca of the Western world, influencing numerous modern languages.
Roman architecture and engineering achievements were monumental. They perfected the arch, vault, and dome, constructing enduring structures like the Colosseum, Pantheon, and aqueducts. These engineering marvels not only showcased Roman ingenuity but also served practical purposes, from public entertainment to water supply.
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1. Detection and Classification of Non-
Proliferative Diabetic Retinopathy Stages
Using Morphological Operations and SVM
Classifier
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Mohammed Shafeeq Ahmed
Research Scholar,
Dept. of Computer Science,
Research and Development Center, Bharathiar University, Coimbatore
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Overview
1. Introduction
1. What is Diabetic Retinopathy
2. Classification of Diabetic Retinopathy
2. Material Collected
3. Methodology
4. Experimental Results
5. Conclusion
6. References
3. • Peoples with diabetes can have an eye disease called diabetic retinopathy.
• This is when high blood sugar levels cause damage to blood vessels in the
retina. These blood vessels can swell and leak. Or they can close, stopping
blood from passing through. Sometimes abnormal new blood vessels grow
on the retina. All of these changes can steal your vision.
• Diabetic retinopathy is a term used for all the abnormalities of the small
blood vessels of the retina caused by diabetes, such as weakening of blood
vessel walls or leakage from blood vessels.
• Diabetic retinopathy is a complication of diabetes that results from damage
to the blood vessels of the light-sensitive tissue at the back of the retina.
• Diabetic retinopathy affect anyone who has type 1 diabetes or type 2
diabetes. The longer a patient has diabetes, the more likely they are to
develop diabetic retinopathy.
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INTRODUCTION
What is Diabetic Retinopathy?
4. • There are two main stages of diabetic eye disease.
• Non-proliferative retinopathy is a common, usually mild form that generally
does not interfere with vision. Abnormalities are limited to the retina and
usually will only interfere with vision if it involves the macula. If left untreated
it can progress to proliferative retinopathy
• Proliferative retinopathy, the more serious form, occurs when new blood
vessels branch out or proliferate in and around the retina. It can cause bleeding
into the fluid-filled center of the eye or swelling of the retina, and lead to
blindness.
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Diabetic Retinopathy
A normal retina
A retina showing a sign
of diabetic retinopathy
5. This is the early stage of diabetic eye disease. Many people with diabetes have
it.
Stage 1 : Mild Non-proliferative Retinopathy.
At this earliest stage, microaneurysms occur. They are small areas of balloon-
like swelling in the retina's tiny blood vessels.
Stage 2 : Moderate Non-proliferative Retinopathy.
As the disease progresses, some blood vessels that nourish the retina are
blocked.
Stage 3 : Severe Non-proliferative Retinopathy
Many more blood vessels are blocked, depriving several areas of the retina
with their blood supply. These areas of the retina send signals to the body to
grow new blood vessels for nourishment.
Stage 4 : Proliferative Retinopathy.
At this advanced stage, the signals sent by the retina for nourishment trigger
the growth of new blood vessels which are abnormal and fragile. They grow
along the retina and along the surface of the clear, vitreous gel that fills the
inside of the eye. By themselves, these blood vessels do not cause symptoms
or vision loss. However, they have thin, fragile walls. If they leak blood,
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Classification of Non-Proliferative
Diabetic Retinopathy (NPDR)
6. The implementation of the detection method proposed was performed in
MATLAB. The accuracy of the method was tested in public database of fundus
images DIARETDB1. The DIARETDB1 has a total of 89 (RGB) fundus
images of size 1500 x 1152. Out of this total, 84 images have characteristic
signs of DR, such as microaneurysms, hemorrhages and exudates, and 5
images are of normal retinas.
Further we have conducted the experiment on different dimension like (4288 x
2848), (786 x 584), captured from different camera and DRIVE dataset. To
check the performance of the proposed algorithm.
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Material Collection
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Methodology
Feature Extraction
SVM Classifier
Pre-processing
Applying Morphological Method
Input Fundus Images
Mild Moderate Severe
Work Flow of NPDR Classification
•We proposed an efficient method for
automatic detection and classification of
NPDR stages using morphological
operations and SVM classifier.
•We perform experiments on a large
dataset collected from the publicly
available DIARETDB1 and DRIVE
database.
•In the proposed method, blood vessels,
MAs and exudates are used to detect DR
stages. The technique used is by dividing
the fundus image into four quadrants and
the area of MAs and exudates is computed
at each of the four quadrants. SVM
classifier is used to identify the different
stages of NPDR in fundus image.
8. Preprocessing:
•Input images is converted to a standard size to improve the quality of input
image.
•Green channel is used for feature extraction of DR from fundus images.
•Contrast enhancement of the output image is done using CLAHE approach.
Blood Vessel Detection:
•Morphological operation is used for extraction of blood vessels and detection
and elimination of Optic Disc.
•Segmentation is used to eliminate other features like MAs and exudates.
•Last the area of blood vessels is calculated.
•Blood vessels area is more in affected retina as compare to the normal retina.
•Hence we classifies as mild, moderate and severe NPDR.
Microaneurysms Detection:
•Edge detection is achieved by using Canny Edge detector.
•Thresholding technique is used to eliminate noise and exudates.
•The resultant image is divided into four quadrants and compute the area
occupied by MAs in each quadrants.
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9. Exudates Detection:
•Exudates are detected using Morphological operations.
•Normal retinal image does not contain any exudates.
•Mild NPDR may contain exudates, but only in one quadrant.
•Moderate NPDR are affected with exudates and distributed in at least two
quadrants.
•Severe NPDR are affected with numerous exudates and present in almost all
the quadrants.
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10. • The proposed method has been evaluated using 129 fundus images
collected from the DIARETDB1 and DRIVE database.
• All the three features of DR have been detected successfully.
• In the normal images the blood vessels occupy the larger area and MAs and
exudates are absent.
• In the case of mild NPDR and moderate NPDR, the MAs and exudates
showed their presence and in severe NPDR their prominence is more.
• The SVM classifier has been used for classification.
• The features were classified as normal, mild NPDR, moderate NPDR, and
severe NPDR.
• An average accuracy of 100%, 93.33%, 100% and 86.67% is obtained for
normal, mild NPDR, moderate NPDR, and severe NPDR, respectively. The
sensitivity of 96.08% and specificity of 97.92% is observed. The details of
the classification obtained are presented in Table1.
• Drawback: The recognition results in the case of sever NPDR is low
compared to other stages since many of the fundus images with severe
NPDR were misclassified as moderate NPDR.
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Experimental Results
15. • An automated system was developed to detect and classify the NPDR
stages from the fundus images using SVM classifier and achieved a high
percentage of sensitivity and specificity.
• The adopted methodology is more efficient and effective than other
methods.
• We have used 129 fundus images collected from the DIARETDB1 and
DRIVE databases.
• The performance of the method for detection and classification of NPDR
stages from fundus images has achieved a high successful percentage.
• This method is highly flexible to other databases.
• The system has classified the NPDR stages in normal, mild NPDR,
moderate NPDR and severe NPDR with an average accuracy of 95%, an
average sensitivity of 96.08% and an average specificity of 97.92%.
• The main purpose of this proposed work is to help the ophthalmologists in
screening the DR using the SVM classifier.
• Thus, this SVM technique has given a successful DR screening method
which helps to detect the disease in early stage and prevents vision loss.
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Conclusions
16. 1. Report on the Expert Committee on the Diagnosis and Classification of Diabetes Mellitus, Diabetes Care, July 1997.
2. American Diabetes Association, Diagnosis and Classification of Diabetes Mellitus Diabetes Care, Jan 2013.
3. Sicree R, Shaw J, Zimmet P. Diabetes & Impaired Glucose Tolerance. Baker IDI Heart and Diabetes Ins. IDF Diabetes Atlas. 4th ed.
Diabetes and IGT, 2009.
4. Ramachandran A, Snehalatha C, Kapur A, Vijay V, Mohan V, Das AK, et al. Diabetes Epidemiology Study Group In India (DESI): High
prevalence of diabetes and impaired glucose tolerance in India:National Urban Diabetes Survey. Diabetologia 2001.
5. Mohan V, Mathur P, Deepa R, Deepa M, Shukla DK, Menon GR, et al. Urban rural differences in prevalence of self-reported diabetes in
India: The WHO-ICMR Indian NCD risk factor surveillance. Diab Res Clin Pract 2008.
6. Chow CK, Raju PK, Raju R, Reddy KS, Cardona M, Celermajer DS, et al. The prevalence and management of diabetes in rural IDC
2006.
7. http://www.icoph.org/downloads/Diabetic-Retinopathy-Detail.pdf.
8. D Welfer, J. Scharcanski, D. R. Marinho, A coarse-to- fine strategy for automatically detecting exudates in color eye fundus images,
Computerized Medical Imaging and Graphics, 2010.
10. U R Acharya, C M Lim, E Y K Ng, C Chee and T Tamura, Computer-based detection of diabetic retinopathy stages using digital fundus
images: Part h: journal of engineering in Medicine, 2009.
11. Niemeijer, M, van Ginneken, B., Staal, J., Suttorp-Schulten, M., and Abramoff, M. Automatic detection of red lesions in digital color
fundus photographs. IEEE transmed imaging, 2005.
12. J Nayak, P S Bhat, U R Acharya, C M Lim, and M Kagathi, Automated Identification of Diabetic Retinopathy Stages Using Digital
Fundus Images. J.Med. Systems, 2008.
13. H. Li and O. Chutatape, “Automated feature extraction in color retinal images by a model based approach,” IEEE Trans. on Medical
Engineering, 2004.
17. C. I. Sanchez, M. Garcia, A. Mayo, M. Lopez and R. Hornero, “Retinal image analysis based on mixture models to detect hard exudates,”
Medical Image Analysis, 2009.
18. AkaraSopharak, BunyaritUyyanonvara, Sarah Barman proposed an “Automatic exudates detection for diabetic retinopathy screening”,
2009.
19. http://www.it.lut.fi/project/imageret/diaretdb1/
20. http://www.isi.uu.nl/Research/Databases/DRIVE
21. Mohammed Shafeeq Ahmed and Dr. B. Indira, “Automatic detection and identification of exudates in retinal fundus images using
morphological techniques”, International Conference on Recent Trends in Engineering and Material Sciences, 2016.
22. Rafael C. Gonzalez, Richard Eugene Woods, Steven L. Eddins “Digital image processing using Matlab” Pearson Education India.
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Reference