Good

1. || JAI SRI GURUDEV ||
SRI ADICHUNCHANAGIRI SHIKSHANA TRUST ®
SJB INSTITUTE OF TECHNOLOGY
BGS HEALTH & EDUCATION CITY, KENGERI, BENGALURU-560060
DEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING
Under the Guidance of
Mrs Rajatha
Associate Professor
Dept. of CSE
Presented By:
Manasa M [1JB21CS414]
Punith HN [1JB21CS419]
Rahul V Sanka [1JB21CS420]
Sarath Kumar V [1JB21CS422]
Project Work Phase – I
Presentation On
“Severity detection of Diabetic retinopathy Using
Deep Learning Algorithms”
Severity detection of Diabetic retinopathy using ML 1

2. AGENDA CONTENTS
1. Abstract
2. Introduction
3. Literature Survey
4. Problem Statement
5. Challenges
6. Motivation
7. Objectives
8. Design and Architecture
9. Methodology
10.Implementation
11.Conclusion
12.References
Severity detection of Diabetic retinopathy using ML
1

3. ABSTRACT
There are several deep learning techniques that are used to perform the predictive analytics over big
data in various medical tasks. Predictive analytics in medical healthcare is a challenging task yet
ultimately helping the practitioners handle big data-informed timely decisions about patient’s
medical health and treatment. This project discusses the predictive analytics in healthcare. Patient’s
medical record is obtained for experimental research. The two architectures of deep learning are
implemented. Performance and accuracy of these applied algorithms are implemented and
compared. Different deep learning techniques used in this research that reveals which algorithm is
best suited for the prediction of diabetes over the patient. This project aims to help doctors and
practitioners in early stage to predict diabetic retinopathy using deep learning techniques.
Severity detection of Diabetic retinopathy using ML
2

4.  Healthcare industry is a very large and sensitive meta data and must be carefully
handled. One of the growing extremely fatal diseases all over the world is
Diabetes Mellitus.
 Some of the useful deep learning techniques for examining the data from diverse
perspectives and synopsizing it into valuable information. The accessibility and
availability of huge amounts of data are able to provide us useful knowledge
unless certain data mining techniques are applied to it.
 The Deep Learning consists of an algorithm called Convolutional Neural
Network(ConvNet/CNN) which gets the input image, assign importance to
various aspects/objects in that image and be able to distinguish one from the other.
INTRODUCTION
Severity detection of Diabetic retinopathy using ML
3

5. Severity detection of Diabetic retinopathy using ML

6.  Mild nonproliferative diabetic retinopathy: This is the earliest stage of diabetic retinopathy,
characterized by tiny areas of swelling in the blood vessels of the retina. These areas of swelling
are known as micro aneurysms. Small amounts of fluid can leak into the retina at this stage,
triggering swelling of the macula. This is an area near the center of the retina.
 Moderate nonproliferative diabetic retinopathy: Increased swelling of tiny blood vessels starts
Severity detection of Diabetic retinopathy using ML 5

7. Severity detection of Diabetic retinopathy using ML 7

8. Severity detection of Diabetic retinopathy using ML
6

9. Authors Year of
publication
Title of the paper Methodology Limitations
Balla Goutam
Mohammad
Farukh Hashmi
(Senior
Member,Ieee),
Zong
WooGeem ,
(Senior
Member, Ieee),
and Neeraj
DhanrajBokde
2022 A Comprehensive
Review of Deep
Learning
Strategies in Retinal
Disease Diagnosis
Using Fundus Images
The proposed review focuses mainly
on providing in depth review of
various DL strategies recently
implemented
for retinal disease diagnosis using
fundus images. This study
also intends to outline possible
future directions for new
researchers interested in AI-based
retinal disease diagnosis.
The models trained on IDRID ,
Messidor, DRIVE datasets may
not perform well on other
datasets. These may not be
suitable for efficient model
training,
JingWang, Liu
Yang, Zhanqian
Huo, Weifeng
He, and Junwei
Luo
2020 Multi-Label
Classification of Fundus
Images With
EfficientNet.
The purpose of this study is to
establish a framework for automatic
identification of multilabel fundus
diseases, and to achieve it by
designing a corresponding ensemble
model.
The amount of data for some
diseases is very limited, which
makes it very difficult to
improve the performance of a
network. Another basic
limitation comes from the black
box of the nature of deep
networks. The network
automatically learns features
from images, but the specific
features learned are unknown.
LITERATURE SURVEY
Severity detection of Diabetic retinopathy using ML 9

10. Ayesha Kazi, Prerna
Sukhija, Miloy Ajmera,
Kailas Devadkar
2021 Processing Retinal
Images to Discover
Diseases
This paper aims to not only
accurately classify the disease
into one of the three possible
abnormalities or assert that
it is a healthy retina. The image may
fall into more than
one category. For example, a retina
which shows any
presence of Diabetic Retinopathy
could also show hints of Glaucoma
It cannot determine the
presence of a variation in
the image
(like highlighting,
tessellation etc) preceding
the
classification step, for
training the neural network,
and to
use unique combinations
for different cases.
Juan Carrillo
, Lola Bautista, Jorge
Villamizar, Juan Rueda,
Mary Sanchez and
Daniela Rueda
2021 Glaucoma Detection
Using Fundus Images
of The Eye
This work presents a computational
tool for automatic glaucoma
detection from fundus images of the
eye. This work propose a
novel method for cup segmentation,
which shows an improvement
in the accuracy compared to other
methods
The vessels segmentation
requires an
improvement due to some
fails in different images and
residual
noise after the
segmentation.
Severity detection of Diabetic retinopathy using ML 10

11. PROBLEM STATEMENT
The problem addressed in this study is the need for a cost-effective and scalable solution for the
detection of diabetic retinopathy (DR), a leading cause of blindness in adults with diabetes.
Current methods for DR detection are time-consuming, expensive, and require specialized
equipment and trained personnel, making them inaccessible in many regions. The objective of this
study is to develop and evaluate a deep learning model for automated DR detection from retinal
images, which can improve the accessibility and affordability of DR screening and diagnosis
Severity detection of Diabetic retinopathy using ML 8

12. CHALLENGES
Severity detection of Diabetic retinopathy using ML 12
Designing a severity detection system for diabetic retinopathy using AI and machine learning involves
several challenges. Here are some of the common challenges faced in this domain:
1. Limited and Imbalanced Data:
- Insufficient and imbalanced datasets can hinder the training of effective models. Diabetic retinopathy
severity levels may not be evenly distributed, leading to biases in the model.
2. Annotation Variability:
- Annotating the severity levels of diabetic retinopathy can be subjective, and different experts may
provide varying annotations. Achieving a consensus among annotators and ensuring a standardized dataset
is challenging.
3. Interpretability and Explain ability:
- AI models, particularly deep learning models, are often considered black boxes. It's crucial to interpret
and explain the decisions made by the model, especially in medical applications where transparency is
essential for gaining trust from healthcare professionals.
4. Generalization Across Diverse Populations:
- Models trained on data from a specific population may not generalize well to other populations with
different demographics, ethnicities, or healthcare practices. Robustness and generalizability across diverse
patient groups are critical considerations.

13. Severity detection of Diabetic retinopathy using ML 13
5. Integration with Clinical Workflow
- Integrating AI systems into the existing clinical workflow can be challenging. Ensuring that the
severity detection system seamlessly fits into the diagnostic process and complements the workflow of
healthcare professionals is crucial for successful implementation.
6. Ethical and Legal Considerations
- Handling patient data raises ethical concerns, and there are legal and privacy considerations when
working with medical information. Compliance with data protection regulations and the establishment of
secure systems are essential.
7. Real-time Processing
- In clinical settings, real-time processing is often required. Designing models that can provide timely
and accurate predictions is challenging, especially when dealing with high-resolution medical images.
8. Robustness to Image Quality
- Medical images can vary in quality due to factors such as equipment differences, variations in lighting
conditions, and patient characteristics. Ensuring that the model is robust to variations in image quality is
essential for reliable predictions.
9. Continuous Learning and Adaptability
- Healthcare data is dynamic, and patterns may change over time. Designing models that can adapt to
new data and continuously learn from evolving patterns in diabetic retinopathy is a significant challenge.

14. Severity detection of Diabetic retinopathy using ML 14
MOTIVATION
The motivation behind developing a deep learning model for diabetic retinopathy detection is the
significant burden of this disease on patients, healthcare systems, and society. Early detection and
treatment of DR are critical to preventing blindness and improving patient outcomes. However,
current methods for DR detection are expensive and inaccessible in many regions, leading to
delays in diagnosis and treatment. By developing a cost-effective and scalable solution for DR
detection using deep learning, this study aims to improve the accessibility and affordability of DR
screening and diagnosis, ultimately improving patient outcomes and reducing the burden on
healthcare systems.

15. OBJECTIVES
1.To develop a deep learning model for automated diabetic retinopathy (DR) detection from
retinal images.
2. To compare the performance of the deep learning model with other state-of-the-art DR
detection methods to assess its superiority and effectiveness.
3. 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.
4. To identify any limitations or challenges faced in the development and evaluation of the deep
learning model, such as limited sample size or biased data, that may affect the generalizability
of the model.
5. To provide recommendations for future research in the field of DR detection using deep
learning, such as the implementation and validation of the model in real-world clinical settings.
Severity detection of Diabetic retinopathy using ML 7

16. DESIGN AND ARCHITECTURE
A system architecture for diabetic retinopathy detection using deep
learning may involve several components, including:
 1. Image acquisition: A retinal camera or imaging device is used to
acquire high-quality retinal images of patients.
 2. Preprocessing: Image preprocessing techniques such as noise
reduction, contrast adjustment, and image enhancement are applied to
the retinal images to improve image quality.
 3. Feature extraction: Deep learning algorithms are used to extract
relevant features from the preprocessed images.
Severity detection of Diabetic retinopathy using ML 16

17.  4. Training and validation: A deep learning model is trained on a large
dataset of retinal images with known diabetic retinopathy labels. The
model is validated on a separate dataset to assess its performance.
 5. Testing and evaluation: The trained model is tested on new retinal
images to assess its diagnostic accuracy and performance.
 6. User interface: A user-friendly interface is developed to enable
healthcare professionals to interact with the system and input patient
information.
 7. Patient database: A patient database is maintained to store patient
information and retinal images for future reference.
Severity detection of Diabetic retinopathy using ML 17

18.  8. Reporting: A reporting module is developed to generate reports
summarizing the results of diabetic retinopathy detection and
classification for each patient.
 9. Integration with EHRs: The system is integrated with electronic
health records (EHRs) to facilitate patient care and management.
 10. System maintenance and updates: The system is designed for easy
maintenance and updates to ensure optimal performance and accuracy
over time.
Severity detection of Diabetic retinopathy using ML 18

19. Convolutional Neural Networks
 A CNN is type of a DNN consists of multiple hidden layers such as
convolutional layer, RELU layer, Pooling layer and fully connected a
normalized layer.
 CNN shares weights in the convolutional layer reducing the memory
footprint and increases the performance of the network.
 The important features of CNN lie with the 3D volumes of neurons,
local connectivity and shared weights.
 A feature map is produced by convolution layer through convolution of
different sub regions of the input image with a learned kernel. Then,
anon- linear activation function is applied through ReLu layer to
improve the convergence properties when the error is low.
Severity detection of Diabetic retinopathy using ML 19

20.  In pooling layer, a region of the image/feature map is chosen and the
pixel with maximum value among them or average values is chosen as
the representative pixel so that a 2x2 or 3x3 grid will be reduced to a
single scalar value. This results a large reduction in the sample size.
Sometimes, the traditional Fully-Connected (FC) layer will be used in
conjunction with the convolutional layers toward the output stage
Severity detection of Diabetic retinopathy using ML 20

21.  A CNN is composed of several kinds of layers:
 Convolutional layer:
Creates a feature map to predict the class probabilities for each feature by
applying a filter that scans the whole image, few pixels at a time.
 Pooling layer (down-sampling):
scales down the amount of information the convolutional layer generated for
each feature and maintains the most essential information.
 Fully connected input layer:
flattens the outputs generated by previous layers to turn them into a single
vector that can be used as an input for the next layer.
 Fully connected layer:
Applies weights over the input generated by the feature analysis to predict an
accurate label.
Severity detection of Diabetic retinopathy using ML 21

22. Severity detection of Diabetic retinopathy using ML 22
Convolutional Neural Network General Architecture

23. DATA FLOW DIAGRAM
Severity detection of Diabetic retinopathy using ML 23

24. METHODOLOGY
Severity detection of Diabetic retinopathy using ML 14

25. Detecting Diabetic Retinopathy:
 Diabetic retinopathy can be detected by using the Retina of the eyes. The eyes having
the retina, inside the retina lots of blood vessels are present.
 The retina of diabetic patients is different from the normal patient. The blood vessels
are chocked inside the retina that indicate the patient has a diabetic.
 By using cnn we have detected the leaking blood vessels.
 By using pixels images are classified into the part of color sheds. If the blood vessels
anywhere choked or leaked then that classified image easily detected the changes into
the image.
 Then the algorithm detects if the patient has Diabetic Retinopathy or not.
Severity detection of Diabetic retinopathy using ML 15

26. IMPLEMENTATION
Steps for Implementation
 Front-End Development Using Python Flask:
Modern computer applications are user-friendly. User interaction is
not restricted to console- based I/O. They have a more ergonomic
graphical user interface (GUI) thanks to high-speed processors and
powerful graphics hardware.
These applications can receive inputs through mouse clicks and can
enable the user to choose from alternatives with the help of radio
buttons, dropdown lists, and other GUI elements.
Severity detection of Diabetic retinopathy using ML 26

27.  Flask Programming:
 Flask is the standard GUI library for Python. Python when
combined with Flask provides a fast and easy way to create GUI
applications. Flask provides a powerful object- oriented interface
to the Tk GUI toolkit. Flask has several strengths.
 It’s cross- platform, so the same code works on Windows,
macOS, and Linux. Visual elements are rendered using native
operating system elements, so applications built with Flask look
like they belong on the platform where they’re run.
Severity detection of Diabetic retinopathy using ML 27
 The goals of implementation are as follows.
Minimize the memory required.
Maximize output readability.
Maximize source text readability.
Minimize the number of source statements.
Minimize development time

28. Severity detection of Diabetic retinopathy using ML 28
Conversion from RGB to grayscale

29.  Advantages of converting RGB color space to gray
 To store a single-color pixel of an RGB color image we will need
8*3 = 24 bits (8 bit for each color component).
 Only 8 bit is required to store a single pixel of the image. So we
will need 33 % less memory to store grayscale image than to store
an RGB image.
 Grayscale images are much easier to work within a variety of task
like In many morphological operation and image segmentation
problem, it is easier to work with single layered image (Grayscale
image) than a three-layered image (RGB color image).
 It is also easier to distinguish features of an image when we deal
with a single layered image
Severity detection of Diabetic retinopathy using ML 29

30. CONCLUSION
 Diabetic retinopathy is a leading cause of blindness in diabetic patients, and
early detection and treatment are crucial for preventing vision loss. Deep
learning algorithms have shown great potential in detecting and classifying
diabetic retinopathy from retinal images, which can aid in the early diagnosis
and management of the disease. The proposed system for diabetic retinopathy
detection using deep learning has the potential to provide accurate and timely
diagnoses, which can improve patient outcomes and reduce healthcare costs.
While there are still challenges to be addressed, such as the need for large and
diverse datasets and interpretability of deep learning models, the advancements
in this field continue to offer promise for improving diabetic retinopathy
detection and treatment.
Severity detection of Diabetic retinopathy using ML 28

31. REFERENCE
 1. Abràmoff, M. D., Lou, Y., Erginay, A., Clarida, W., Amelon, R., Folk, J. C., & Niemeijer, M.
(2016). Improved automated detection of diabetic retinopathy on a publicly available dataset
through integration of deep learning. Investigative Ophthalmology & Visual Science, 57(13),
5200-5206.
 2. Bhaskaranand, M., Ramachandra, C., Bhat, S., & Cuadros, J. (2020). Automated diabetic
retinopathy screening and monitoring using deep learning. Journal of Healthcare Engineering,
2020, 1-13.
 3. Burlina, P. M., Joshi, N., Pekala, M., Pacheco, K. D., Freund, D. E., Bressler, N. M., & Wong,
T. Y. (2017). Automated grading of age-related macular degeneration from color fundus images
using deep convolutional neural networks. JAMA ophthalmology, 135(11), 1170-1176.
 4. Gulshan, V., Peng, L., Coram, M., Stumpe, M. C., Wu, D., Narayanaswamy, A., ... & Webster,
D. R. (2016). Development and validation of a deep learning algorithm for detection of diabetic
retinopathy in retinal fundus photographs. Jama, 316(22), 2402-2410.
 5. Kermany, D. S., Goldbaum, M., Cai, W., Valentim, C. C., Liang, H., Baxter, S. L., ... &
Keane, P. A. (2018). Identifying medical diagnoses and treatable diseases by image-based deep
learning. Cell, 172(5), 1122-1131.
 6. Li, Z., Keel, S., Liu, C., He, Y., Meng, W., Scheetz, J., ... & Ting, D. S. (2020). An automated
grading system for detection of vision-threatening referable diabetic retinopathy on the basis of
color fundus photographs. Diabetes care, 43(8), 1748-1755.
Severity detection of Diabetic retinopathy using ML 29

32.  7. Osareh, A., Shadgar, B., & Markham, R. (2012). A survey of computer-aided
diagnosis of ocular diseases. Computer Methods and Programs in Biomedicine, 108(1),
407-433.
 8. Rajalakshmi, R., Subashini, R., Anjana, R. M., Mohan, V., & Deepa, M. (2018).
Automated diabetic retinopathy detection in smartphone-based fundus photography
using artificial intelligence. Eye, 32(6), 1138-1144.
 9. Ramachandra, C., Bhat, S., & Bhaskaranand, M. (2018). Automated grading of
diabetic retinopathy using deep neural network. Journal of Healthcare Engineering,
2018, 1-14.
 10. Schmidt Erfurth , U., Bogunovic, H., Sadeghipour, A., & Schlegl, T. (2018).
Artificial intelligence in retina. Progress in retinal and eye research, 67, 1-29.
Severity detection of Diabetic retinopathy using ML 30

33. THANK YOU
Severity detection of Diabetic retinopathy using ML 33