  A variety of vision ailments are associated with geographic atrophy (GA) in
the foveal region of the eye. In current clinical practice, the ophthalmologist
manually detects potential presence of such GA based on fundus autofluorescence
(FAF) images, and hence diagnoses the disease, when relevant. However, in view
of the general scarcity of ophthalmologists relative to the large number of
subjects seeking eyecare, especially in remote regions, it becomes imperative
to develop methods to direct expert time and effort to medically significant
cases. Further, subjects from either disadvantaged background or remote
localities, who face considerable economic/physical barrier in consulting
trained ophthalmologists, tend to seek medical attention only after being
reasonably certain that an adverse condition exists. To serve the interest of
both the ophthalmologist and the potential patient, we plan a screening step,
where healthy and diseased eyes are algorithmically differentiated with limited
input from only optometrists who are relatively more abundant in number.
Specifically, an early treatment diabetic retinopathy study (ETDRS) grid is
placed by an optometrist on each FAF image, based on which sectoral statistics
are automatically collected. Using such statistics as features, healthy and
diseased eyes are proposed to be classified by training an algorithm using
available medical records. In this connection, we demonstrate the efficacy of
support vector machines (SVM). Specifically, we consider SVM with linear as
well as radial basis function (RBF) kernel, and observe satisfactory
performance of both variants. Among those, we recommend the latter in view of
its slight superiority in terms of classification accuracy (90.55% at a
standard training-to-test ratio of 80:20), and practical class-conditional
costs.
