Diabetic Retinopathy using image processing

Diabetic Retinopathy is a serious eye condition that can develop in people with diabetes. To create a PowerPoint presentation on "Diabetic Retinopathy," I'll outline the key points for each slide to help you effectively convey the topic's importance and details.

1. DIABETIC RETINOPATHY DETECTION USING DEEP LEARNING UNDER THE GUIDANCE OF Dr.Sujata mallapur By HAFSAH KULSUM SW22SCS003

2. CONTENTS Abstract Introduction Existing System and Disadvantages Proposed System and Advantages Literature Survey System Requirements Modules System architecture(Flow Chart) Methodology Output Conclusion References

3. 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

4. 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.

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

6. 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 Classification performance is poor accuracy is less

7. 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

8. 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]

9. SYSTEM SPECIFICATION AND DESIGN 3.1 HARDWARE CONFIGURATION Processor : Pentium Core I5 11 th 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

10. 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.

11. 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.

12. 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.

13. 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.

14. 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

15. 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: Noise reduction; Gradient calculation; Non-maximum suppression; Double threshold; Edge tracking by hysteresis.

16. 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.

17. 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.

18. SVM ALGORITHM

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

20. 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.

21. 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.

22. 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.

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