  One of the most serious corneal disorders, keratoconus is difficult to
diagnose in its early stages and can result in blindness. This illness, which
often appears in the second decade of life, affects people of all sexes and
races. Convolutional neural networks (CNNs), one of the deep learning
approaches, have recently come to light as particularly promising tools for the
accurate and timely diagnosis of keratoconus. The purpose of this study was to
evaluate how well different D-CNN models identified keratoconus-related
diseases. To be more precise, we compared five different CNN-based deep
learning architectures (DenseNet201, InceptionV3, MobileNetV2, VGG19,
Xception). In our comprehensive experimental analysis, the DenseNet201-based
model performed very well in keratoconus disease identification in our
extensive experimental research. This model outperformed its D-CNN equivalents,
with an astounding accuracy rate of 89.14% in three crucial classes:
Keratoconus, Normal, and Suspect. The results demonstrate not only the
stability and robustness of the model but also its practical usefulness in
real-world applications for accurate and dependable keratoconus identification.
In addition, D-CNN DenseNet201 performs extraordinarily well in terms of
precision, recall rates, and F1 scores in addition to accuracy. These measures
validate the model's usefulness as an effective diagnostic tool by highlighting
its capacity to reliably detect instances of keratoconus and to reduce false
positives and negatives.
