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Content
• Abstract
• Introduction
• Literature survey
• Problem statement
• Requirements analysis
• Flow chart
• References
Abstract
Diabetic retinopathy (DR) is a leading cause of blindness worldwide. Early detection and
effective management are crucial in preventing irreversible damage to the retina. In recent
years, deep learning techniques have shown promise in aiding the detection and diagnosis
of DR. This paper aims to investigate the potential of using convolutional neural networks
(CNNs) for automated DR screening. The study utilized a publicly available dataset of retinal
images to train and test the performance of a CNN model for DR detection and classification.
Results showed that the proposed model achieved high accuracy in both tasks,
outperforming existing state-of-the-art methods. The findings suggest that deep learning
algorithms can aid in the early detection and management of DR, potentially reducing the
burden of the condition on healthcare systems and improving patient outcomes.
Introduction
Diabetic retinopathy is caused by an elevated blood glucose level which
leads to blood vessels clogging, rupture and leak.
After suffering from diabetes for 25 years, the occurrence of symptoms of
DR is typical to 80-100%.
The initial stage of diabetic retinopathy is characterized by the narrowing of
the vessel walls and the decrease of blood flow.
In the final stage, the exudates are visible in large clusters and more blood
vessels are rapidly forming causing frequent bleeding.
3 17ISP85
After this stage, the patient suffers from a
complete loss of vision and peeling of the retina.
They often describe seeing floating dark spots
and a distorted image caused by blood leaking
from damaged vessels.
Symptoms of the disease begin to appear only at
later stages, when the patient becomes aware of
impaired vision.
4 17ISP85
Literature Survey
5
17ISP78
Title of the paper and year Methodology Merits Demerits
1. Early prediction of Diabetes
Mellitus
Year: 2020 ICRITO
This work demonstrate the
development of convolutional neural
network that takes image as input and
predicts the diabetic retinopathy.
It also describe the development and
implementation of ConvNet based
algorithm for diabetic.
Collects data quickly and accurately
Shows the description of the data set.
Presents the precision and sensitivity
(recall) outcome.
This is the real world noxious disease
.
The early diagnosis of this disease is
always a challenging problem.
2. Prevention Of Diabetes By Devising
A Prediction Analysis Model
Year: 2020 IEEE
The goal of this paper is to mimic
learning by experience like human
and achieve tasks with external
intervention and machine learning
algorithm by seven steps & they are
finding the issues, setting up the
information, picking the learning
strategy, applying the learning
technique, evaluating technique,
streamlining, announcing the
outcomes.
It can predict diabetes according to
the guidelines.
It optimize the performance of the
network.
Multiple value are required.
Finding mistake is difficult.
3. Recent Advancements and Future
Prospects on E-nose Sensors
Technology And Machine Learning
Approaches For Non- Invasive
Diabetes Diagnosis:
Year: 2020 IEEE
This paper tells us the E-nose
technique, this combines the sensory
unit & data processing ML algorithm.
To improve the performance concept
of Deep Learning is applied by
modifying convolution Neural
Network for one dimensional breath
signals.
Can monitor continuous glucose
monitoring.
This diagnostic technique of blood
glucose level prediction is highly
accurate for detection of diabetes.
This is the painful process.
Inconvenient when multiple reading
are required in a day
6
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Title of the paper and year Methodology Merits Demerits
5. Design of an ANFIS Decision
Support System for Diabetes
Diagnosis
Year: 2020 IEEE
The outcome of this paper is
categorized into five groups i.e very
low, low, medium, high, very high
based on the likelihood and severity
of diabetes. The fuzzy model is cost
effective and reduce complexity.
Display results systematically.
Uses five layers of input hence results
is more accurate.
This process is expensive.
6. Diabetes Mellitus Prediction and
Classifier comparitive study
Year: 2020 PARC
This paper have used five steps to
predict the diabetic or not. They are
Data Collection, Data Pre Processing,
implementation of various model,
evaluation of models, prediction of
diabetes. This process have used SVM
algorithm which provide highest
accuracy.
Mechanism of drugs and therapies
are discussed.
General regressive neural network
approach is also discussed.
Results are different as different
algorithm are used.
7. A Gene Prediction Function for
Type 2 Diabetes Mellitus using
Logistic Regression
Year: 2020 ICICS
Here the studies is concern using the
gene expression with classification
data sets to predict the disease.
Results shows the prediction rate of
70.9% and 70.6% for men and
women.
Shows the average accuracy.
Worked on two datasets hence
accuracy is more.
This is a long process.
Selecting the relevant dataset is
difficult.
8. Performance enhancement of
diabetes prediction by finding
optimum K for KNN classifier with
feature selection method.
Year: 2020 ICSSIT
This model is using both KNN and
SVM algorithm, and the accuracy for
KNN is 85.8% and SVM 88.56%.
They are using the dataset which is
developed by a model in WEKA.
Can determine type 1 and type 2
Diabetes easily.
KNN is only good for small datasets.
Performance can be more improved.
Requires whole dataset hence space
and time required is more.
Problem statement
• The current screening methods for diabetic retinopathy (DR) are
inefficient, leading to delayed diagnosis and poor disease management.
Traditional screening techniques require manual examination by trained
professionals, which can be time-consuming, costly, and prone to human
error. This can result in missed diagnoses and delayed treatment, leading
to irreversible damage to the retina and potentially permanent vision loss.
• Moreover, as the prevalence of diabetes continues to rise globally, the
burden on healthcare systems for DR screening is becoming increasingly
significant. Therefore, there is a pressing need for an automated and
efficient DR screening method to improve early detection and
management of the condition. In recent years, deep learning techniques
have shown promise in aiding the diagnosis of DR, and this study aims to
investigate their potential for automated screening.
flowchart
System Requirements
 Hardware Requirements:
1. Processor: Minimum Pentium 2.266 MHz
2. RAM :128 Mb
3. Disk space : 124Mb
 Software Requirements:
1. Dataset: Retina Images
2. Language: Python
3. Technologies and tools:
Open CV
Keras and Tensorflow
4. Model: CNN
9 17ISP85
References
[1] 2020 8th International Conference on Reliability, Infocom Technology and Optimization (ICRITO)
[2] 2020 International Congress on human Computer Interaction and Robotic Application (HORA)
[3] DOI 10.1109/RBME.2020.2993591, IEEE REVIEWS in Biomedical Engineering
[4] International Conference on communication and Signal Processing, July 28-30, 2020 IEEE
[5[ 2020 International Conference on Power Electronic & IoT Application in Renewable Energy and is control (PARC)
[6] 2020 11th International Conference on information and communication System (ICICS)
[7] Proceeding of the International Conference on smart systems and Inventive Technology (ICSSIT 2020)
[8] N.Sneha and Tarun Gangil. Analysis of diabetes mellitus for early prediction using optimal features selection 2019
[9] John M. Dennis. Precision Medicine in Type 2 Diabetes Using individualized prediction models 2020.
[10] Seokho Kang. Personalized prediction of drug efficiency for diabetes treatment 2018.
10
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diabetic Retinopathy. Eye detection of disease

  • 1. Content • Abstract • Introduction • Literature survey • Problem statement • Requirements analysis • Flow chart • References
  • 2. Abstract Diabetic retinopathy (DR) is a leading cause of blindness worldwide. Early detection and effective management are crucial in preventing irreversible damage to the retina. In recent years, deep learning techniques have shown promise in aiding the detection and diagnosis of DR. This paper aims to investigate the potential of using convolutional neural networks (CNNs) for automated DR screening. The study utilized a publicly available dataset of retinal images to train and test the performance of a CNN model for DR detection and classification. Results showed that the proposed model achieved high accuracy in both tasks, outperforming existing state-of-the-art methods. The findings suggest that deep learning algorithms can aid in the early detection and management of DR, potentially reducing the burden of the condition on healthcare systems and improving patient outcomes.
  • 3. Introduction Diabetic retinopathy is caused by an elevated blood glucose level which leads to blood vessels clogging, rupture and leak. After suffering from diabetes for 25 years, the occurrence of symptoms of DR is typical to 80-100%. The initial stage of diabetic retinopathy is characterized by the narrowing of the vessel walls and the decrease of blood flow. In the final stage, the exudates are visible in large clusters and more blood vessels are rapidly forming causing frequent bleeding. 3 17ISP85
  • 4. After this stage, the patient suffers from a complete loss of vision and peeling of the retina. They often describe seeing floating dark spots and a distorted image caused by blood leaking from damaged vessels. Symptoms of the disease begin to appear only at later stages, when the patient becomes aware of impaired vision. 4 17ISP85
  • 5. Literature Survey 5 17ISP78 Title of the paper and year Methodology Merits Demerits 1. Early prediction of Diabetes Mellitus Year: 2020 ICRITO This work demonstrate the development of convolutional neural network that takes image as input and predicts the diabetic retinopathy. It also describe the development and implementation of ConvNet based algorithm for diabetic. Collects data quickly and accurately Shows the description of the data set. Presents the precision and sensitivity (recall) outcome. This is the real world noxious disease . The early diagnosis of this disease is always a challenging problem. 2. Prevention Of Diabetes By Devising A Prediction Analysis Model Year: 2020 IEEE The goal of this paper is to mimic learning by experience like human and achieve tasks with external intervention and machine learning algorithm by seven steps & they are finding the issues, setting up the information, picking the learning strategy, applying the learning technique, evaluating technique, streamlining, announcing the outcomes. It can predict diabetes according to the guidelines. It optimize the performance of the network. Multiple value are required. Finding mistake is difficult. 3. Recent Advancements and Future Prospects on E-nose Sensors Technology And Machine Learning Approaches For Non- Invasive Diabetes Diagnosis: Year: 2020 IEEE This paper tells us the E-nose technique, this combines the sensory unit & data processing ML algorithm. To improve the performance concept of Deep Learning is applied by modifying convolution Neural Network for one dimensional breath signals. Can monitor continuous glucose monitoring. This diagnostic technique of blood glucose level prediction is highly accurate for detection of diabetes. This is the painful process. Inconvenient when multiple reading are required in a day
  • 6. 6 17ISP78 Title of the paper and year Methodology Merits Demerits 5. Design of an ANFIS Decision Support System for Diabetes Diagnosis Year: 2020 IEEE The outcome of this paper is categorized into five groups i.e very low, low, medium, high, very high based on the likelihood and severity of diabetes. The fuzzy model is cost effective and reduce complexity. Display results systematically. Uses five layers of input hence results is more accurate. This process is expensive. 6. Diabetes Mellitus Prediction and Classifier comparitive study Year: 2020 PARC This paper have used five steps to predict the diabetic or not. They are Data Collection, Data Pre Processing, implementation of various model, evaluation of models, prediction of diabetes. This process have used SVM algorithm which provide highest accuracy. Mechanism of drugs and therapies are discussed. General regressive neural network approach is also discussed. Results are different as different algorithm are used. 7. A Gene Prediction Function for Type 2 Diabetes Mellitus using Logistic Regression Year: 2020 ICICS Here the studies is concern using the gene expression with classification data sets to predict the disease. Results shows the prediction rate of 70.9% and 70.6% for men and women. Shows the average accuracy. Worked on two datasets hence accuracy is more. This is a long process. Selecting the relevant dataset is difficult. 8. Performance enhancement of diabetes prediction by finding optimum K for KNN classifier with feature selection method. Year: 2020 ICSSIT This model is using both KNN and SVM algorithm, and the accuracy for KNN is 85.8% and SVM 88.56%. They are using the dataset which is developed by a model in WEKA. Can determine type 1 and type 2 Diabetes easily. KNN is only good for small datasets. Performance can be more improved. Requires whole dataset hence space and time required is more.
  • 7. Problem statement • The current screening methods for diabetic retinopathy (DR) are inefficient, leading to delayed diagnosis and poor disease management. Traditional screening techniques require manual examination by trained professionals, which can be time-consuming, costly, and prone to human error. This can result in missed diagnoses and delayed treatment, leading to irreversible damage to the retina and potentially permanent vision loss. • Moreover, as the prevalence of diabetes continues to rise globally, the burden on healthcare systems for DR screening is becoming increasingly significant. Therefore, there is a pressing need for an automated and efficient DR screening method to improve early detection and management of the condition. In recent years, deep learning techniques have shown promise in aiding the diagnosis of DR, and this study aims to investigate their potential for automated screening.
  • 9. System Requirements  Hardware Requirements: 1. Processor: Minimum Pentium 2.266 MHz 2. RAM :128 Mb 3. Disk space : 124Mb  Software Requirements: 1. Dataset: Retina Images 2. Language: Python 3. Technologies and tools: Open CV Keras and Tensorflow 4. Model: CNN 9 17ISP85
  • 10. References [1] 2020 8th International Conference on Reliability, Infocom Technology and Optimization (ICRITO) [2] 2020 International Congress on human Computer Interaction and Robotic Application (HORA) [3] DOI 10.1109/RBME.2020.2993591, IEEE REVIEWS in Biomedical Engineering [4] International Conference on communication and Signal Processing, July 28-30, 2020 IEEE [5[ 2020 International Conference on Power Electronic & IoT Application in Renewable Energy and is control (PARC) [6] 2020 11th International Conference on information and communication System (ICICS) [7] Proceeding of the International Conference on smart systems and Inventive Technology (ICSSIT 2020) [8] N.Sneha and Tarun Gangil. Analysis of diabetes mellitus for early prediction using optimal features selection 2019 [9] John M. Dennis. Precision Medicine in Type 2 Diabetes Using individualized prediction models 2020. [10] Seokho Kang. Personalized prediction of drug efficiency for diabetes treatment 2018. 10 17ISP78