Affiliations: Computer Vision and Soft Computing Laboratory, School of Computing, SASTRA Deemed University, Thanjavur, Tamil Nadu, India
Correspondence:
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Corresponding author. Ramakrishnan Sundaram, Computer Vision and Soft Computing Laboratory, School of Computing, SASTRA Deemed-to-be-University, Thanjavur, Tamil Nadu, India. E-mail: srk@ict.sastra.edu.
Abstract: This paper proposes a prediction system to identify the type of eye diseases like glaucoma and diabetic retinopathy. The proposed system processes the images captured using the fundus camera that is connected to the computer. The acquired fundus images are fed into the proposed prediction system which can be deployed in the cloud, and it identifies the type of disease. This forms a cyber-physical system. Underdeveloped countries which do not have the necessary infrastructure can utilize this service when this system is deployed in the cloud. For identifying these diseases, ophthalmologists extract parameters manually from the fundus image, which is a difficult task. Hence, this research work attempts to develop a system to automate the feature extraction from fundus images and with the extracted features, eye diseases are predicted. From the literature, it is found that many research works were focused on the binary classification of any one disease. In this paper, a novel classification methodology is proposed that helps the experts and clinicians to classify Diabetic Retinopathy, Glaucoma and healthy eye images with more accuracy. The proposed system with high accuracy is designed with the following phases: i) image acquisition, ii) image enhancement, iii) local features extraction using Speeded Up Robust Feature (SURF), iv) Bag of Features/Visual Words (BoF/BoVW) obtained through k-means clustering of local features, and v) classification using Error-Correcting Output Code (ECOC) linear SVM. It is inferred from the results that proposed method of classification using BoVW provided a maximum accuracy of 92% when compared to other state-of-the-art recent literature.
Keywords: Fundus image, image enhancement, bag of features, support vector
machine, classification
