Abstract:
Retinal disease is a global health crisis that impacts millions of individuals annually. Early
illness diagnosis is an important field of medical research since eye problems can greatly
affect patients' eyes and overall well-being. A plethora of data for model training and
improved model designs have led to the rapid development of deep learning techniques
for computer vision applications. This research will describe a powerful convolutional
neural network (CNN) model that uses ablation studies to categorize eye disease
illnesses. The disease is colored, and the overall quality is increased by reducing noise,
improving the image, and removing artifacts. The ODIR 5k dataset of retinal images is
freely available to the public. Due to improvement techniques, the quantity of images
depicting eye diseases has increased. At the outset, the enhanced dataset suggests a basic
CNN model. The robust CNN model, which I propose, was derived from an ablation
investigation. To train the model, a library of publicly available images depicting eye
illnesses is utilized. In terms of accuracy (92%), area under the curve (AUC) (99%), and
KAPPA (91%), the recommended robust model outperformed the competition. The model
is robust and performs admirably when applied to fresh data. The model also achieves
good recall and precision when used to identify binary and multi-class eye disorders.
