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.
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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.
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5. Literature Survey
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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
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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.
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
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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.
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