Classification of Diabetic Retinopathy.pptx

Statistical Texture Features based Automatic Detection and Classification of Diabetic Retinopathy

1. Statistical Texture Features based Automatic Detection and Classification of Diabetic Retinopathy Presented By: Supervised By: Co-Supervised By: Md. Rahat Khan ID: 2015105050006 Dept. of CSE, KYAU A. S. M. Shafi Lecturer Dept. of CSE, KYAU Dr Mir Mohammad Azad Professor and Head Dept. of CSE, KYAU

2. Overview Motivation Research Questions Objectives Clinical Background System Architecture Dataset Preprocessing Image Segmentation Feature Extraction Classification Cross Validation Experimental Results Comparison with Existing Methods Discussion Conclusion Limitations Future Work

3. 3 Motivation Diabetic Retinopathy (DR) is the fifth leading cause of global blindness. The total number of people with diabetes is projected to rise from 171 million in 2000 to 366 million in 2030. Detection of DR is a time-consuming and manual process that requires digital color fundus photographs of the retina . The number with vision-threatening DR will increase from 37.3 million to 56.3 million, if any proper action is not taken.

4. 4 Research Questions The specific problem statement for this thesis is : “ Can machine learning be used for automatic detection and classification of diabetic retinopathy? ” However , the following research questions would facilitate the achievement of this thesis: Is this approach to detect and classify diabetic retinopathy? Are the system’s accuracy acceptable? Can the method reduce the cognitive burden on a qualified doctor?

5. 5 Objectives “ Effectively use machine learning to detect and classify diabetic retinopathy from retinal images. ” Other intensions of the current study include the followings: S tudy the terms and features related to DR. A novel approach to utilize machine learning features. Drastically reducing the cognitive burden of a qualified physician. The proposed approach is favorable and effective which achieves the best performance of accuracy.

6. Diabetic retinopathy (DR) is a medical condition where the retina is damaged because of fluid leaks from blood vessels into the retina. 6 Clinical Background 02 01 Non-Proliferative Diabetic Retinopathy Proliferative Diabetic Retinopathy M ainly occurs when most of the blood vessels in the retina close, preventing enough blood flow .  These new blood vessels are abnormal and do not supply the retina with proper blood flow . T he earliest stage of diabetic retinopathy where damage blood vessels in the retina begin to leak extra fluid and small amounts of blood into the eye.

7. 7 System Architecture -------------------------------------------------------------------------------------------------------------------------------------------------------- Input Images   Preprocessing   Segmentation Feature Extraction   Classification   Assessment   Retinal Image   Resize Image Dark and bright region detection using blood vessel extraction GLCM GLRLM SVM, KNN, RF   Accuracy , Sensitivity, Precision, F1-score   -------------------------------------------------------------------------------------------- -------------------------------------------------------------------------------------------- -------------------------------------------------------------------------------------------- -------------------------------------------------------------------------------------------- -------------------------------------------------------------------------------------------- -------------------------------------------------------------------------------------------- Figure 1: A framework of the proposed approach. SVM: Support Vector Machine KNN : K-Nearest Neighbors RF: Random Forest GLCM: Gray Level Co-occurrence Matrix GLRLM: Gray Level Run Length Matrix

8. 8 Proposed Algorithm -------------------------------------------------------------------------------------------------------------------------------------------------------- Algorithmic Steps: Capture the digital fundus image as input image. Pre-process all input image into a uniform size. Apply Kirsch’s template technique to extract blood vessels from the preprocessed image. Execute second-order and higher-order statistical texture feature algorithm on the segmented image found from step 3. Generate a Feature Vector (FV) for each training image. Implement three classifiers (SVM, KNN, and RF) to test the train image for classification and evaluate the performance of the results.

9. 9 Dataset -------------------------------------------------------------------------------------------------------------------------------------------------------- “The proposed system utilizes more than 600 different retinal images”. Important sources of database include: Indian Diabetic Retinopathy Image Dataset ( https ://idrid.grand-challenge.org/Data / ). Kaggle Dataset ( https:// www.kaggle.com/c/diabetic-retinopathy-detection ). Table 1: Our dataset: The distribution of retinal images used in our proposed system . Type Short Name Number of Images Size Non-Proliferative Diabetic Retinopathy NPDR 167 246MB Proliferative Diabetic Retinopathy PDR 340 367MB Normal Image - 136 150MB

10. 10 Preprocessing -------------------------------------------------------------------------------------------------------------------------------------------------------- The original image is resized into a standard size of 565x375 format to allow faster calculation. (a) (b) Figure 2: Preprocessing : (a) input image, and (b) preprocessed image .

11. Image Segmentation To extract the blood vessels from the preprocessed image, Kirsch template is used as a image segmentation technique . It is used to detect the edge of blood vessel by using the eight direction of template which rotated fairly by 45 °. From the templates result, the greater will be considered for the output of products and then extracted. Figure 3 shows the arrays of Kirsch’s templates. 11 5 -3 -3 -3 -3 5 -3 -3 -3 -3 5 5 -3 -3 -3 5 5 5 -3 -3 -3 5 -3 -3 5 5 -3 -3 5 -3 -3 -3 -3 -3 5 5 -3 -3 -3 -3 5 5 5 -3 -3 -3 -3 5 5 5 -3 -3 -3 -3 5 5 5 5 -3 5 -3 -3 -3 -3 0° 45° 90° 135° 180° 225° 270° 315° Figure 3 : Example of Kirsch’s templates .

12. Image Segmentation (cont’d) 12 Figure 4: Retinal vessel segmentation results.

13. Feature Extraction The process to represent raw image in a reduced form to facilitate decision making such as pattern detection, classification or recognition . Feature extraction techniques: Second Order Statistical Texture Features Higher Order Statistical Texture Features 13

14. Second Order Statistical Texture Feature 14 The Gray Level Co-occurrence Matrix (GLCM) method is a way of extracting second order statistical texture features. The GLCM functions are used for finding texture properties of an image by calculating the frequency of occurrence of pixel pairs with specific values and in a specific spatial relationship. Figure 5 : Example of the creation of a GLCM matrix. 4*4 image 1 2 1 3 1 3 2 2 4 2 1 1 1 1 3 4 GLCM Matrix 2 1 3 2 1 1 1 1

15. Second Order Statistical Texture Feature ( cont’d ) 15 Contrast Correlation Energy (ASM) Entropy Inverse Difference Moment Contrast Correlation Energy (ASM) Entropy Inverse Difference Moment Sum Entropy Difference Entropy Sum Entropy Difference Entropy G is the number of gray levels used. μ is the mean value of P. μx , μy , ∂x and ∂y are the means and standard deviations of Px and Py . Px ( i ) is the ith entry in the marginal matrix obtained by summing rows of P( i , j). Sum of Squares Sum Average Sum of Squares Sum Average

16. Second Order Statistical Texture Feature ( cont’d ) 16 Table 2 : GLCM features computed from retinal images. Feature Non-Proliferative image Proliferative image Normal Image Contrast 0.049079996 0.080073074 0.1245 Correlation 7.37215018 11.84084557 14.485 Energy 0.481351097 0.261918084 0.3242 Entropy 1.114384043 1.624658084 1.4874 Homogeneity 0.978856348 0.963758729 0.9593 Sum of Square 7.322934412 11.78826862 14.4349 Sum Average 5.186458425 6.183003155 7.0898 Sum Entropy 1.075093217 1.570173178 1.4239 Difference Entropy 0.181596513 0.268884514 0.3070

17. Higher-Order Statistical Texture Feature 17 The Gary Level Run Length Matrix (GLRLM) method is a way of extracting higher order statistical texture features. The run length is the number of pixels in the run, and the run length value is the number of times such a run occurs in an image. Figure 6: Design of the GLRLM matrix from a 4 × 4 image with 5 gray levels . 4*4 images 1 2 2 3 1 2 3 3 4 2 4 1 4 1 2 3 GLRLM Matrix 4 3 1 2 1 3

18. Higher-Order Statistical Texture Feature (cont’d) 18 A run-length matrix Q( i,j ) for a given image is defined by the specifying direction and then count the occurrence of a run for each gray levels i and run-length j in this direction . Here,  is the number of pixels and  denotes the total number of runs.   Short Run Emphasis (SRE) Long Run Emphasis (LRE) Low-Gray-Level Run Emphasis (LGRE) Short Run Emphasis (SRE) Long Run Emphasis (LRE) Low-Gray-Level Run Emphasis (LGRE) . High-Gray-Level Run Emphasis (HGRE) Gray-Level Non-uniformity (GLN) Run-Length Non-uniformity (RLN) Run Percentage (RP) High-Gray-Level Run Emphasis (HGRE) Gray-Level Non-uniformity (GLN) Run-Length Non-uniformity (RLN) Run Percentage (RP)

19. Higher-Order Statistical Texture Feature (cont’d) 19 . Table 3 : GLRLM feature computed from retinal images. Features Name Non-Proliferative Images Proliferative Images Normal Image SRE 0.389088 0.289583   0.31226433 LRE 380.3482   685.2044 555.9815 GLN 20546.53 12397.85   11294.03 RP 0.468957 0.369963 0.431806 RLN 16407.24 8078.537   10661.73 LGRE 68.67707 67.51923   65.09879 HGRE 20546.53 12397.85   11294.03

20. Classification 20 Support Vector Machine (SVM): SVM is a binary classifier based on the concept of a hyperplane that defines decision boundaries. Figure 8 : A linear support vector machine. Random Forest (RF): Random forest is a classifier that operates by constructing a multitude of decision trees at training time and outputting the class that is the mode of the classes or mean prediction of the individual trees. Figure 9: Random f orest classifier.

21. Classification (cont’d) 21 K-Nearest Neighbor (KNN): A simple classifier that stores all available cases and classifies new cases based on a similarity measure (e.g., distance functions )   Figure 10: KNN classifier Choosing the optimal value for K is best done by first inspecting the data. In general, a large K value is more precise as it reduces the overall noise but there is no guarantee. Historically , the optimal K for most datasets has been between 3-10. That produces much better results than 1NN.

22. Cross Validation 22 K-Fold Cross Validation: Shuffle the dataset randomly. Split the dataset into k groups . Figure 11: K-fold cross-validation process. For each unique group: Take the group as a hold out or test data set. Take the remaining groups as a training data set. Fit a model on the training set and evaluate it on the test set. Retain the evaluation score and discard the model. Summarize the skill of the model using the sample of model evaluation scores.

23. Experimental Results 23 To evaluate the performance of the proposed method, we have examined four metrics per class: Sensitivity ( Sen ), Precision, F1-score and Accuracy ( Acc ). Table 6: Formula for evaluation scheme . TP = True Positives; TN = True Negatives; FP = False Positives; FN = False Negatives Sensitivity (Recall) TP / (TP + FN) Precision TP / TP + FP F1-score 2 * Recall * Precision / Recall + Precision Accuracy TP + TN/(TP + TN + FP + FN) Table 5 : Confusion matrix for two-class classification problem . Predicted Class Positive Negative Actual Class Positive TP FN Negative FP TN

24. Experimental Results (cont’d) 24 Table 7: Confusion matrix of SVM. PDR NPDR Normal PDR 326 11 4 NPDR 19 136 12 Normal 4 3 129 Sensitivity Predicted class Actual class Figure 12: Performance of the prediction models with SVM classifier. Precision F1-score Accuracy 93% 91% 89% 96% 81% 95% 94% 86% 92% 94.1% 93.01% 96.43%

25. Experimental Results (cont’d) 25 87% OPEN TICKETS Table 8: Confusion matrix of KNN (K=3 ). PDR NPDR Normal PDR 302 23 16 NPDR 21 127 19 Normal 18 14 104 Predicted class Actual class Figure 13: Performance of the prediction models with KNN (K=3) classifier. Sensitivity Precision F1-score Accuracy 89% 77% 75% 89% 76% 76% 89% 77% 76% 87.89% 88.04% 89.6%

26. Experimental Results (cont’d) 26 87% OPEN TICKETS Table 9: Confusion matrix of KNN ( K=5). PDR NPDR Normal PDR 308 17 16 NPDR 21 136 10 Normal 18 19 99 Predicted class Actual class Figure 14: Performance of the prediction models with KNN (K=5) classifier. Sensitivity Precision F1-score Accuracy 89% 79% 79% 90% 81% 73% 90% 80% 76% 88.82% 89.6% 90.22%

27. Experimental Results (cont’d) 27 87% OPEN TICKETS Table 10: Confusion matrix of RF. PDR NPDR Normal PDR 333 5 3 NPDR 6 149 12 Normal 2 3 131 Predicted class Actual class Figure 15: Performance of the prediction models with RF classifier. Sensitivity Precision F1-score Accuracy 98% 95% 90% 98% 89% 96% 98% 92% 93% 97.52% 95.92% 96.89%

28. Experimental Results (cont’d) 28 87% OPEN TICKETS Table 11. Sensitivity , precision, F1-score and accuracy of each classifier Image Type SVM KNN (K=3) KNN (K=5) RF Sn Pre F1 Acc Sn Pre F1 Acc Sn Pre F1 Acc Sn Pre F1 Acc PDR 93 96 94 94.1 89 89 89 87.89 89 90 90 88.82 98 98 98 97.52 NPDR 91 81 86 93.01 77 76 77 88.04 79 81 80 89.6 95 89 92 95.92 Normal 89 95 92 96.43 75 76 76 89.6 79 73 76 90.22 90 96 93 96.89

29. Experimental Results (cont’d) 29 87% OPEN TICKETS Table 12: Weighted measure of each classifier Name of the classifier Weighted Measure Sn Pre F1-score Acc SVM 91.63 91.89 91.35 94.30 KNN (K=3) 82.93 82.88 83.14 88.29 KNN (K=5) 83.66 84.07 84.45 89.31 RF 95.53 96.45 95.38 95.19

30. Comparison with Existing Methods 30 Table 13: Comparison among different methods. Authors Methodology Dataset Result (Overall Accuracy) Khademi et. al Shift-invariant Discrete Wavelet Transform 86 82.2% S. Manker et. al Morphological operations 107 89.50% Qureshi et. al Convolutional Neural Network (CNN) + RF 125 97.5% Neto et. al Unsupervised coarse-to-fine algorithm 60 87% Sarathi et. al Ellipse fitting 63 92% Garcia et. al CNN 35,126 83.68% Proposed Method Statistical texture features + RF 644 95.19%

31. Discussion 31 We incorporate both s econd-order and higher-order statistical texture features with linear SVM, KNN, and RF classifier . We consider the texture feature because it gives us more details about specific regions in an image. The proposed system for diabetic retinopathy classification showed that the use of statistical features achieves almost high weighted sensitivity (95.53%) and, equally importantly, displays high weighted precision ( 96.45%) and weighted F1-score (95.38%) with RF classifier.

32. Conclusion 32 The results of the extensive experimental study have to lead us to the following clear conclusions : The use of statistical texture features on the retinal image results in higher classification accuracy in terms of sensitivity, precision, and F1-score. GLCM and GLRLM provide information about the connected length of a particular pixel in a definite direction. The use of RF positively affects the discrimination of normal and abnormal samples.

33. Limitation 33 Lack of publicly available datasets . The high difference in various image qualities. High interclass similarity and intraclass variation . C oding has developed as a top-down programming in each step, which caused the processing to take long. We believe that with a larger and more representative training set, better results in the classification stage could have been obtained .

34. Future Work 34 In the future, the method should be tested on larger datasets to correctly evaluate the algorithm. Future perspectives of this work include the improvement of retinal images by applying super-resolution.

35. 35 IJIRT IJARCSEE IJSER IJIRSET IJECE SSRG IJC ASRJETS IJCET Accepting J ournal Names: Acceptance

36. 36 THANKS! Any questions ?