Machine learning

1. VISVESVARAYA TECHNOLOGICAL UNIVERSITY
“JNANASANGAMA”, BELAGAVI-590014, KARNATAKA, INDIA
IMPACT COLLEGE OF ENGINEERING AND APPLIED SCIENCES
DEPARTMENT OF ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING
Sahakarnagar, Bengaluru-560092

2. PROJECT WORK PHASE-1
DIABETIC RETINOPATHY
DIAGNOSIS USING RETINAL
FUNDUS IMAGES
PRESENTED BY:
Chandan N (1IC20AI001)
Harini S (1IC20AI002)
Payal Sharma (1IC20AI007)
UNDER THE GUIDANCE OF:
Mrs. Pooja
ASSISTANT PROFESSOR
DEPT OF CS&E

3. CONTENTS
Abstract
Introduction
Literature survey
Problem statement
Requirements analysis
Flow chart
References

4. 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.

5. 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.

6.  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.

7. LITERATURE SURVEY
SL
NO
TITLE OF THE
PAPER AND YEAR
AUTHORS MERITS DEMERITS METHODOLOGY
1. Early prediction of
Diabetes Mellitus
Year: 2020 ICRITO
Jing Wang, Liu
Yang, Zhanqiang
Huo , Weifeng He,
and Junwei Luo
• 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 that is
harmful and prediction
is a struggle.
• The early diagnosis of
this disease is always a
challenging problem.
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.
2. Prevention Of Diabetes By
Devising A Prediction
Analysis Model
Year: 2020 IEEE
Balla Goutam
, Mohammad
Farukh Hashmi
, (Senior Member,
Ieee),
Zong Woo Geem ,
(Senior Member,
Ieee), and Neeraj
Dhanraj Bokde
• It can predict
diabetes according
to the guidelines.
• It optimize the
performance of the
network.
• Multiple values are
required.
• Finding mistake is
difficult.
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.

8. SL
NO
TITLE OF THE PAPER
AND YEAR
AUTHORS MERITS DEMERITS METHODOLOGY
3. Recent Advancements and Future
Prospects on E-nose Sensors
Technology And Machine Learning
Approaches For Non- Invasive
Diabetes Diagnosis:
Year: 2020 IEEE
Juan Carrillo,
Lola Bautista,
Jorge Villamizar,
Juan Rueda,
Mary Sanchez
and Daniela
Rueda
• 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
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.
4. Design of an ANFIS Decision
Support System for Diabetes
Diagnosis
Year: 2020 IEEE
Ayesha Kazi,
Prerna Sukhija,
Miloy Ajmera,
Kailas Devadkar
• Display results
systematically.
• Uses five layers of
input hence results
is more accurate.
• This process is
expensive.
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.
5. Diabetes Mellitus Prediction and
Classifier comparitive study
Year: 2020 PARC
Ramachandra,
C., Bhat, S., &
Bhaskaranand,
M.
• Mechanism of
drugs and therapies
are discussed.
• General regressive
neural network
approach is also
discussed.
• Results are different as
different algorithm are
used.
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

9. SL
NO
TITLE OF THE PAPER
AND YEAR
AUTHORS MERITS DEMERITS METHODOLOGY
6. A Gene Prediction Function for
Type 2 Diabetes Mellitus using
Logistic Regression
Year: 2020 ICICS
Schmidt-Erfurth,
U., Bogunovic,
H., Sadeghipour,
A., & Schlegl, T.
• Shows the average
accuracy.
• Worked on two
datasets hence
accuracy is more.
• This is a long process.
• Selecting the relevant
dataset is difficult.
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.
7. Performance enhancement of
diabetes prediction by finding
optimum K for KNN classifier with
feature selection method.
Year: 2020 ICSSIT
Rajalakshmi, R.,
Subashini, R.,
Anjana, R. M.,
Mohan, V., &
Deepa, M.
• Can determine
type 1 and type 2
of diabetic
• Diabetes easily.
• KNN is only good for
small datasets.
• Performance can be
more improved.
• Requires whole
dataset hence space
and time required is
more.
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.

10. SYSTEM REQUIREMENTS
Hardware Requirements:
 Processor : Intel Minimum Pentium 2.266 MHz
 RAM : 128 Mb
 Disk space : 124Mb
Software Requirements:
 Dataset : Retina Images
 Language : Python
 Technologies and tools : Open CV Keras and Tensorflow
 Model : CNN

11. EXISTING SYSTEM
Apart from a binocular model for the various classes of Diabetic
Retinopathy detection task is also trained and evaluated to further prove
the effectiveness of the binocular design. The final result shows that, on
a 10% validation set, the binocular model achieves a kappa score of
0.829 which is higher than that of existing non ensemble model. In the
end the analogy between confusion matrices obtained through models
with paired and unpaired inputs is performed and it demonstrates that
the binocular architecture does improve the classification performance.

12. PROBLEM STATEMENT
 The current screening methods for diabetic retinopathy (DR) are inefficient,
leading to delayed diagnosis and poor disease management. Traditional screening
techniques require 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.

13. OBJECTIVES
The objectives of this study are:
 To develop a deep learning model for automated diabetic retinopathy (DR) detection from retinal images.
 To evaluate the performance of the deep learning model using standard techniques, such as backpropagation and gradient
descent, and performance metrics such as accuracy, sensitivity, and specificity.
 To compare the performance of the deep learning model with other state-of-the-art DR detection methods to assess its
superiority and effectiveness.
 To assess the clinical implications of the developed deep learning model, including early detection and treatment of DR,
reducing the workload of ophthalmologists, and improving patient outcomes.

14. PROPOSED SYSTEM
The scope of this study is to develop and evaluate a deep learning model
for automated diabetic retinopathy (DR) detection from retinal images.
The study will use publicly available retinal image datasets with labeled
DR severity to train and validate the deep learning model. The deep
learning model will use a combination of convolutional neural networks
(CNNs) and recurrent neural networks (RNNs) for feature extraction,
sequence modeling, and classification of DR severity.The developed deep
learning model can have significant clinical implications, including
early detection and treatment of DR, reducing the workload of
ophthalmologists, and improving patient outcomes.

15. PROPOSED SYSTEM ARCHITECTURE

16. PROPOSED METHODS
DATA COLLECTION
PREPROCESSING
DATA SPLITTING
FEATURE EXTRACTION
VALIDATION
HYPER-PARAMETER
TUNING
MODEL TRAINING
DEEP LEARNING MODEL
SELECTION

17. 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.

18. THANK YOU