  In light of the expanding population, an automated framework of disease
detection can assist doctors in the diagnosis of ocular diseases, yields
accurate, stable, rapid outcomes, and improves the success rate of early
detection. The work initially intended the enhancing the quality of fundus
images by employing an adaptive contrast enhancement algorithm (CLAHE) and
Gamma correction. In the preprocessing techniques, CLAHE elevates the local
contrast of the fundus image and gamma correction increases the intensity of
relevant features. This study operates on a AMDNet23 system of deep learning
that combined the neural networks made up of convolutions (CNN) and short-term
and long-term memory (LSTM) to automatically detect aged macular degeneration
(AMD) disease from fundus ophthalmology. In this mechanism, CNN is utilized for
extracting features and LSTM is utilized to detect the extracted features. The
dataset of this research is collected from multiple sources and afterward
applied quality assessment techniques, 2000 experimental fundus images
encompass four distinct classes equitably. The proposed hybrid deep AMDNet23
model demonstrates to detection of AMD ocular disease and the experimental
result achieved an accuracy 96.50%, specificity 99.32%, sensitivity 96.5%, and
F1-score 96.49.0%. The system achieves state-of-the-art findings on fundus
imagery datasets to diagnose AMD ocular disease and findings effectively
potential of our method.

Image and Video Processing;Computer Vision and Pattern Recognition