Automated retinal health diagnosis using pyramid histogram of visual words and Fisher vector techniques

Bhandary, Sulatha V (2018) Automated retinal health diagnosis using pyramid histogram of visual words and Fisher vector techniques. Computers in Biology and Medicine, 92. pp. 204-209. ISSN 0010-4825

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Untreated age-related macular degeneration (AMD), diabetic retinopathy (DR), and glaucoma may lead to irreversible vision loss. Hence, it is essential to have regular eye screening to detect these eye diseases at an early stage and to offer treatment where appropriate. One of the simplest, non-invasive and cost-effective techniques to screen the eyes is by using fundus photo imaging. But, the manual evaluation of fundus images is tedious and challenging. Further, the diagnosis made by ophthalmologists may be subjective. Therefore, an objective and novel algorithm using the pyramid histogram of visual words (PHOW) and Fisher vectors is proposed for the classification of fundus images into their respective eye conditions (normal, AMD, DR, and glaucoma). The proposed algorithm extracts features which are represented as words. These features are built and encoded into a Fisher vector for classification using random forest classifier. This proposed algorithm is validated with both blindfold and ten-fold cross-validation techniques. An accuracy of 90.06% is achieved with the blindfold method, and highest accuracy of 96.79% is obtained with ten-fold cross-validation. The highest classification performance of our system shows the potential of deploying it in polyclinics to assist healthcare professionals in their initial diagnosis of the eye. Our developed system can reduce the workload of ophthalmologists significantly.

Item Type: Article
Uncontrolled Keywords: Age-related macular degeneration ; Bag-of-visual-words ; Computer-aided diagnosis system ; Diabetic retinopathy ;Eye diseases ;Fisher vector encoder ;Fundus images Glaucoma Machine learning.
Subjects: Medicine > KMC Manipal > Ophthalmology
Depositing User: KMC Library
Date Deposited: 29 May 2018 05:58
Last Modified: 29 May 2018 05:58

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