G. R. Hemalakshmi*, D. Santhi, V. R. S. Mani, A. Geetha, N. B. Prakash
CMES-Computer Modeling in Engineering & Sciences, Vol.125, No.1, pp. 125-143, 2020, DOI:10.32604/cmes.2020.011331
- 18 September 2020
Abstract Diabetic retinopathy, aged macular degeneration, glaucoma etc. are
widely prevalent ocular pathologies which are irreversible at advanced stages.
Machine learning based automated detection of these pathologies facilitate
timely clinical interventions, preventing adverse outcomes. Ophthalmologists
screen these pathologies with fundus Fluorescein Angiography Images (FFA)
which capture retinal components featuring diverse morphologies such as
retinal vasculature, macula, optical disk etc. However, these images have
low resolutions, hindering the accurate detection of ocular disorders. Construction of high resolution images from these images, by super resolution
approaches expedites the diagnosis of pathologies with better accuracy. This
paper presents a More >
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