Features are essential for any classification or analysis in image processing. There are many types of features which can be extracted from the images each gives its identical informations about the image. Here MA region supposed with properties such as shape, color and size which appears as a dark red colored circle shape. To identify MA and non- MA region feature vectors are formed for each candidate regions.
The classification is the final process which classifies the result (I.e, Normal, Abnormal etc). There are various classifiers used in literature which divides into two classes majorly called as dichotomies and some classifies into multi classes (e.g. decision trees , feedforward ...view middle of the document...
To evaluate the proposed work for the detection of diabetic retinopathy using MAs is performed by using the fundus images taken from publicly avalilable databases such as DRIVE , and DIARETDB1  in MATLAB environment. The results obtained for each step is discussed here.
The datasets specified in table 1 is taken for the evaluation because these dataset images contain varities of DR lesions which suits well for evaluation. The results are taken from both the dataset which contains both normal and abnormal (i.e contains lesions) images that are equally divided for both training and testing.
At first the training set is given to the proposed system which trains the svm classifier by its features and produces the svm structure which is saved seperately. Then testing set is given to the system which follows same procedure as training set till feature extraction. Then in classification, the structured svm which is saved before is loaded and based on this structure, the classifier classifies the testing images individually. The svm classifier produces the result based on its feature values that match approximately with the feature values from the trained images. (For example, we take one single test image features, if its features match in svm classifier with the features of 5 in the trained image, then the result will be 5).
For the evaluation of this proposed work the performance metrics such as Sensitivity (Sen), Specificity (Spec), Positive Predictive Value (PPV) and Accuracy (Acc) is taken.These parameters are calculated using the following equations respectively:
The results presented in the table 2 compares the performace of the classifier SVM and ELM for the detection of MAs. It clearly reveals that proposed technique with ELM classifier outperforms when compared with SVM in Sensitivity, Specificity, PPV and Accuracy for DRIVE dataset.
Here from the table 3, the performace of the classifier SVM and ELM for the detection of MAs is compared. It clearly highlights that proposed technique with ELM classifier outperforms when compared with SVM in Sensitivity, Specificity, PPV and Accuracy for DIARETDB1 dataset.
The execution time is also another factor which must be minimum because any proposed system must satisfy in the means of both efficiency and time.
The above figure shows the comparison of time taken by the proposed work to complete its process for the dataset DRIVE and DIARETDB1. From the figure ELM with the proposed work achives efficient result in reduced time than SVM for the both dataset.
The figure 9 shows the comparison graph of accuracy produced by the proposed work with the classifier SVM and ELM for DRIVE and DIARETDB1 dataset. This graph shows clearly that ELM produces maximum accuracy than SVM for the proposed technique.
Automatic detection of diabetic retinopathy turns out to be active research because of recent spread of this diseases largly. The first manifestation of diabetic retinopathy is...