RETINO GUARDAI:ANEFFICIENTNETMODELANDDEEPLEARNING FRAMEWORKFOR EXPLAINABLE,ANATOMICALLY-AWAREDIABETIC RETINOPATHY SCREENING

Diabetic Retinopathy (DR) remains one of the lead
ing preventable causes of vision loss among working-age adults
with diabetes, and international screening guidelines recommend
regular fundus examination to catch referable disease before
irreversible damage occurs. Manual grading, however, is slow,
requires a trained ophthalmologist, and does not scale to the
size of most diabetic populations, which motivates automated
screening from retinal fundus photographs. This work presents
RetinoGuard AI, an integrated screening pipeline built around a
fine-tuned EfficientNet-B4 backbone for binary DR classification,
reaching 91.4% validation accuracy on the APTOS 2019 Blind
ness Detection dataset with a sensitivity of 92.1% and specificity
of 90.8% on referable cases. Beyond classification, the system
contributes two further components that most published DR
classifiers do not provide: first, a Grad-CAM based explainability
module that produces a pixel-level saliency map showing exactly
which parts of the fundus image drove the prediction; and
second, a Five-Zone Retinal Region Activation Analysis that
aggregates that saliency map over five anatomically named
zones- the macula, optic disc, superior arcade, inferior arcade,
and periphery- condensing a dense heatmap into a small
set of clinically meaningful numbers a reviewer can scan in
seconds. All three components are wired into a single session
authenticated Streamlit web application, so that classification,
visual explanation, and zone-level risk summary are produced
end-to-end from one uploaded image, without a separate offline
analysis step.