Diabetic Retinopathy using image processing

DIABETIC RETINOPATHY
DETECTION USING DEEP
LEARNING
UNDER THE GUIDANCE OF
DR.SUJATA MALLAPUR
BY
HAFSAH KULSUM
SW22SCS003

CONTENTS
1. Abstract
2. Introduction
3. Existing System and Disadvantages
4. Proposed System and Advantages
5. Literature Survey
6. System Requirements
7. Modules
8. System architecture(Flow Chart)
9. Methodology
10.Output
11.Conclusion
12.References

ABSTRACT
 Diabetic retinopathy is a disease caused by uncontrolled chronic diabetes
and it can cause complete blindness if not timely treated. Therefore early
medical diagnosis of diabetic retinopathy and it medical cure is essential
 Manual detection of diabetic retinopathy by ophthalmologist take plenty of
time and patients need to suffer a lot at this time.
 This study proposes a machine learning method for extracting three features
like exudates, hemorrhages, and micro aneurysms and classification using
hybrid classifier which is a combination of SVM,KNN, random forest,
logistic regression, multilayer perceptron network

INTRODUCTION
 Diabetic Retinopathy is a complication that affect the eye due to the result of
high blood glucose called diabetes. It can cause vision loss and in severe
condition can lead to complete blindness.
 Early symptoms of diabetic retinopathy includes blurred vision, darker areas
of vision, eye floaters and difficulty in perceiving colours. Proper detection of
diabetic retinopathy in early stage is extremely important to prevent complete
blindness.
 Available physical tests to detect diabetic retinopathy includes pupil dilation,
visual acuity test, optical coherence tomography, etc. But they are time
consuming and patients need to suffer a lot.

FIGURE 1: (a) PDR (b) Severe NPDR (c) Moderate NPDR (d) Mild NPDR
(e) Normal Retina [7].

EXISTING SYSTEM
 Besides a binocular model for the five class DR detection task is also trained
and evaluated to further prove the effectiveness of the binocular design.
 The 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.
DISADVANTAGES
1. Classification performance is poor
2. accuracy is less

PROPOSED SYSTEM
 For a deep learning model, the most important parts that should be
focused on are data set, network architecture and training method.
 In the proposed method we are implementing hybrid classifier. That is
we are using combination of five classifiers, SVM, KNN Random
forest. Each classifier will classify the total 244 images into either
normal or abnormal image.
ADVANTAGES
 Perfect classification
 Give more accuracy

LITERATURE SURVEY
 [Saravanan, 2013] proposed an automated system for the red lesion
diabetic retinopathy detection based on microaneurysms using GMM
classifier [1].
 [Venkatalakshmi, 2011] described automated system for hard exudate
detection using sharp edge and colour highlights as two features. Methods
involved in the detection process were colour based classification, sharp
edge detection, and extraction of optic disc. [2]

SYSTEM SPECIFICATION AND DESIGN
3.1 HARDWARE CONFIGURATION
Processor : Pentium Core I5 11th
Gen
RAM : 4GB or more.
Hard Disk : 500GB or more.
Monitor : 21 inch Color Monitor
Keyboard : 102/104 Keys
Mouse : Optical Mouse
3.2 SOFTWARE CONFIGURATION
Operating System :Windows 10 /11
Front End :Python
Framework : Pycharm

MODULES
 DATASET
The database was created with images taken from publicly available retinopathy
detection datasets. The Kaggle dataset contain 1000 images From the total images
we have chosen 122 images with diabetic retinopathy and 122 normal images.
 MEDIAN FILTERING
The presence of diabetic retinopathy is based on the appearance, number, spread
and size, area of exudates, microaneurysms, and hemorrhages
 PRE-PROCESSING
In image pre-processing, to find exudates, initially image from dataset is converted
to HSV image. It is useful to extract yellow coloured exudates from RGB image
when we convert RGB to HSV.

 IMAGE SEGMENTATION
After image pre-processing, to segment exudates we have done smoothing,
masking and bit-wise AND. Smoothing is employed to remove high spatial
frequency noise from image. Image blurring is achieved by convolving the
image with a low-pass filter kernel.
 FEATURE EXTRACTION
For binary classification, here we are using 2 features, ie, number of
exudates as first parameter and number of hemorrhages and micro
aneurysms as second parameter.

 CLASSIFICATION
In the proposed method we are implementing hybrid classifier. That is we are
using combination of five classifiers, Support vector machines, K nearest
neighbours, Random forest. Each classifier will classify the total 244 images
into either normal or abnormal image.
 SVM
Support Vector Machine is a supervised machine learning algorithm which is
extensively used for both classification and regression day to day problems .It
is mostly used in classification problems rather than regression problems. In
the SVM algorithm, we will have n number of features.

 KNN: The k-nearest neighbors (KNN) algorithm is a simple and it is easy-to-
implement focused on supervised machine learning algorithm. It is mainly
used to solve both classification and regression problems KNN captures the
idea of similarity which is often called distance / proximity / closeness.
 RANDOM FOREST: Random forest implies it consists of a large number of
individual decision trees. Decision trees are drawn upside down with its root
at the top. In a decision tree, it contains condition/internal node, based on
which the tree splits into branches/ edges.
 VOTING: It is the simplest method of combining the outputs from multiple
machine learning algorithms. Initially we create two or more standalone
machine learning models with our training dataset.

The above system architecture, accepts eye image, applies pre-processing which
converts the image into gray scale and de-noises the image, then applies feature
extraction using canny edge algorithm which finds the edges of the image and
divides the image into number of parts using segmentation. Further detects
retinopathy and classifies the retinopathy using the classifiers such as Decision
tree, svm, random forest and CNN.
SYSTEM ARCHITECTURE

METHODOLOGY
CANNY EDGE ALGORITHM
The Canny edge detector is an edge detection operator that uses a multi-
stage algorithm to detect a wide range of edges in images
The Canny edge detection algorithm is composed to 5 steps:
1. Noise reduction;
2. Gradient calculation;
3. Non-maximum suppression;
4. Double threshold;
5. Edge tracking by hysteresis.

 RANDOM FOREST ALGORITHM
Step 1: Select random samples from a given data or training set.
Step 2: This algorithm will construct a decision tree for every training
data.
Step 3: Voting will take place by averaging the decision tree.
Step 4: Finally, select the most voted prediction result as the final
prediction result.

 DECISION TREE
Step-1: Begin the tree with the root node, says S, which contains the complete
dataset.
Step-2: Find the best attribute in the dataset using Attribute Selection Measure
(ASM).
Step-3: Divide the S into subsets that contains possible values for the best
attributes.
Step-4: Generate the decision tree node, which contains the best attribute.
Step-5: Recursively make new decision trees using the subsets of the dataset
created in step -3. Continue this process until a stage is reached where you
cannot further classify the nodes and called the final node as a leaf node.

OUTPUT
FIGURE 2: INPUT IMAGE FIGURE 3: MEDIAN FILTERING (PREPROCESSING) FIGURE 4: FEATURE EXTRACTION
FIGURE 5: SEGMENTATION FIGURE 6: Classification FIGURE 7: Model Accuracy

CONCLUSION
 For diabetic retinopathy detection, count the number for MA occurred, count
the number of hemorrhages occurred and count the number of exudates
occurred in the image so we can decide the condition of image.
 Then features are calculated and feed to svm, decision tree , Random Forest
classifier. Voting of three classifiers are chosen as final prediction . So from the
extracted feature it directly concludes the disease grade as normal or abnormal.
So earlier detection and diagnosis of diabetic retinopathy help the patients from
blindness and also the severe effects of disease can be decreases.

REFERENCES
 Farrikh Alzami, Abdussalam, Rama Arya Megantara and
Ahmad Zainul Fanani, Diabetic Retinopathy Grade
Classification based on Fractal Analysis and Random Forest,
International Seminar on Application for Technology of
Information and Communication, 2019.
 2. Dinial Utami Nurul Qomariah, Handayani Tjandrasa and
Chastine Fatichah, Classification of Diabetic Retinopathy and
Normal Retinal Images using CNN and SVM, 12th International
Conference on Information and Communication Technology
and System, 2019.

 3. Shailesh Kumar and Basant Kumar Diabetic
Retinopathy Detection by Extracting Area and Number
of Microaneurysms from Colour Fundus Images, 5th
International Conference on Signal Processing and
Integrated Networks, 2018.
 4. Mohamed Chetoui, Moulay A Akhloufi, Mustapha
Kardoucha , Diabetic Retinopathy Detection using
Machine Learning and Texture Features, IEEE Canadian
Conference on Electrical and Computer Engineering,
2018.

Diabetic Retinopathy using image processing

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