Diabetic Nephropathy (DN), a
common and severe complication of diabetes,
can lead to chronic kidney disease and eventual
kidney failure. Early and accurate detection is
essential to enable timely medical intervention
and improve patient outcomes. This study
applies a deep learning approach using the
VGG16 architecture to classify and detect DN
from medical images. Initially pre-trained on
large image datasets, the model was fine-tuned
for kidney ultrasound and tissue image analysis
to improve feature extraction and classification
accuracy. Performance was assessed using
standard evaluation metrics such as accuracy,
precision, recall, F1-score, and AUC-ROC. The
model demonstrated promising results,
outperforming conventional methods in
classification tasks. This work highlights the
potential of deep learning as a non-invasive and
reliable solution for early-stage DN detection,
contributing to better diagnostic support in
healthcare settings.