Computer‐aided diagnosis of retinal diseases using multidomain feature fusion. Issue 2 (7th November 2019)

• Record Type: Journal Article
• Authors: Keerthiveena, B.; Esakkirajan, S.; Selvakumar, K.; Yogesh, T.
• Is Part Of: International journal of imaging systems and technology. Volume 30:Issue 2(2020)
• Page Start: 367
• Page End: 379
• Publication Date: 2019-11-07
• Subjects: feature fusion -- intensity hue saturation -- support vector machine -- wavelet packet transform -- weighted principal component analysis; Imaging systems -- Periodicals; Image processing -- Periodicals; 621.367
• DOI: 10.1002/ima.22379
• Languages: English
• ISSNs: 0899-9457

Abstract

Abstract: In this article, an attempt is made to combine both transform and spatial domain features to increase the accuracy, sensitivity, and specificity for the classification of retinal diseases. Papilloedema, macular edema, glaucoma, diabetic retinopathy (DR), and central retinal vein occlusion (CRVO) are the leading cause of blindness in humans. To identify these diseases in a mass screening process, it consumes intense labor and time. In this proposed work, an automated computer aided algorithm is developed to classify the normal from the abnormal (Papilledema, macular edema, glaucoma, DR, and CRVO) images. For any classification task, an increase in data size, number of classes, and dimension of the feature space affect the performance of any classifier. A single classifier is generally unable to handle the wide variability and scalability of the data in any problem. In the proposed algorithm, the feature fusion technique is employed to reduce the input data to the classifier. The features are extracted from wavelet packet transform (WPT) and intensity hue saturation (IHS). A support vector machine (SVM)‐based learning algorithm is used to train the classifier using the extracted features. The proposed method yields better results than the existing classifier fusion method with an overall accuracy, sensitivity, specificity, positive prediction value (PPV), negative prediction value (NPV), and area under ROC curve (AUC) of 96.3%, 95.8%, 97.8%, 96.4%, 98.1%, and 0.971 respectively.