Diabetic retinopathy is a leading cause of blindness that can be detected through automated analysis of fundus images. The document proposes using support vector machines to build a model that can robustly detect four key features of diabetic retinopathy - hard exudates, soft exudates, microaneurysms, and hemorrhages. The model is trained on a standardized set of fundus images and achieves over 95% accuracy on classification, providing an affordable solution to diagnose a disease affecting many people.

1. DIABETIC RETINOPATHY
USING SUPPORT VECTOR MACHINES
Submitted By:
Prateek Goyal 10102219
Ayush Garg 10104660

2. Introduction
Diabetes is one of the primary causes of causing blindness, almost 5% of the
total cases in the world can be attributed to it.
For a proper cure, a proper diagnosis is also very necessary.
Due to this major concern, we provide an automated system to robustly detect
diabetic retinopathy from the fundus images thereby providing a chance to
save the vision of many people.

3. • To provide, the vast amount of population
affected by diabetic retinopathy a proper
diagnosis.
• Need to provide a cheap and robust solution in
the field.
Why this domain ???

4. People face the following problems:
Expensive Diagnostic
No availability of such techniques in the eye clinics.
Testing must correspond to the strict regulations in the medical practices.
Hypertension can onset retinopathy, so that must also be taken into account
Why consider diabetic retinopathy ?
We are living in the world where modernization is bringing non-
invasive techniques with the help of computer science and
technology for diagnosis of diseases

5. What is diabetic retinopathy ?
Diabetic retinopathy (DR) is an eye disease caused by the complication of
diabetes and we should detect it early for effective treatment. DR can be
a complication of diabetes type 1 or diabetes type 2.
As diabetes progresses, the vision of a patient may start
deteriorate and lead to diabetic retinopathy.

6. • DIARETDB1 diabetic retinopathy database and evaluation protocol.
(TOMI KAUPPI, VALENTINA KALESNYKIENE, ET AL., 2011)
• SVM and Neural Network based Diagnosis of Diabetic Retinopathy.
(R.Priya, P. Aruna, 2012)
• Object Recognition and Detection by a Combination of Support Vector Machine
and Rotation Invariant Phase Only Correlation
(CHIKAHITO NAKAJIMA, NORIHIKO ITOH, ET AL. , 2000).
Previous work
We utilize a standard set of fundus images to train our SVM to detect
DR with respect to the 4 features present during the ailment.

7. Since this field is a current research one, we
take the opportunity to categorize a significant
part of the existing work in a way that can help
doctors in making the right decision.

8. we will deal with the following techniques
Solution to the big problem !!!
Image Processing
Feature Classification
Support Vector Machines
Feature Extraction

9. • Color Eye Image: The original fundus image.
• Split Image into individual channels
• Adaptive Histogram: Histogram equalization highlighting the darker regions and keeping the
brighter regions at almost same level.
• Median Blur to remove noisy elements.
• Thresholding to implement spatial filters.
• Hough Circle Transform
• Morphological Closing: Dilation followed by an erosion, to close the gaps hiding random features.
• SVM classifier
Implementation

10. Components
1. Input – Input is an image of RGB format.
2. Label – Final result obtained from the input image. 0 indicates disease is not
there,1 indicates person is suffering from the disease.
3. Feature extractor- It takes input and applies the features to it.
4. Machine learning Algorithm- Supervised algorithms like SVM which take
features as input.
5. Classifier Model- It is a model which actually predicts the output based on the
training set which was earlier fed to the SVM.

11. Feature Extractor - It takes “Input” i.e. image(s) as input and creates a set of
feature List for each image.
Before sending the images to feature extractor they must be preprocessed
Gray scale
Histogram equalization
Discrete wavelet transformation
Filtering to remove noise/unwanted
components
Edge detection
Feature
Extractor
Feature
Extractor

12. Feature extractor gives a feature vector. It is a list of features.
In our case these features come out to be the problems arising in the retina of the eye:-
• Hard Exudates
• Soft Exudates
• Micro Aneurysms
• Hemorrhages

13. Machine Learning Algorithms
A supervised machine learning algorithm
analyzes the training data and produces an
inferred function, which can be used for mapping
new examples.
Supervised
Machine
Learning
Algorithm

14. A Support Vector Machine (SVM) is a
discriminative classifier formally defined by a
separating hyperplane. In other words, given
labeled training data (supervised learning),
the algorithm outputs an optimal hyperplane
which categorizes new examples.

15. OBTAINED ACCURACY:
Classifiers Accuracy
Support Vector Machines 95.6%
PCNN 89.6%
• This result is obtained from SVM and Neural Network based Diagnosis of Diabetic
Retinopathy.
(R.Priya, P. Aruna, 2012)
Therefore we use SVM in our predictions as in medical cases accuracy is our prime concern.
Thus, providing us the final accuracy of >95%.

16. THANK YOU !!!