A Pre-Trained VGG16 Model for Cataract Eye Disease Prediction

A cataract is a common eye condition that predominantly affects the eye's crystalline lens, resulting in a diminishing of its natural transparency and a gradual decline in visual sharpness. This clouding of the lens can hinder the capacity to perceive objects clearly and difficulty in carry out everyday tasks effortlessly. Early and precise diagnosis of this disease plays a crucial role for securing vision quality and avoiding life-threatening issues. This paper proposes a novel method for detecting cataract disease at early stages using a convolutional neural network-based VGG16 model. This technique has achieved a remarkable accuracy of 96.10% on the cataract and normal eye disease dataset collected from Kaggle. This model has been fine-tuned by keeping the value of the learning rate at 0.01 and the accuracy has been measured at epochs 5, 10, 15, and 20. The loss value of the proposed odel has been also measured which identified that the model shows the least loss of 0.1651 at epoch 20. This study also highlights the potential of deep learning to revolutionize the detection of eye diseases while simultaneously highlighting the significance of moral and cooperative approaches to the quest for better healthcare solutions.