A Cataract is the most common cause of sight impairment around the globe. Therefore, determining the presence and severity of cataracts is critical for analysis and movement monitoring. This research proposes a framework for the automatic classification of cataracts through retinal fundus images. Moreover, this research provides an automatic structure for diagnosing and analyzing eye disease. Furthermore, image augmentation is utilized to increase the number of images in the dataset. In addition, pre-processing is used to remove the noise in the images. Convolutional Neural Networks, GoogleNet, and AlexNet are proposed in this research for the classification of normal and cataract-affected images. A total of 449 cataract and normal fundus images are acquired for testing and training the models. The dataset was increased to 1159 images using a data augmentation technique. Exploratory outcomes demonstrate that the GoogleNet attained 97% accuracy, 90% sensitivity, and 90% specificity. On the other hand, AlexNet attained 90% accuracy, 88% sensitivity, and 85% specificity. Outcomes show that GoogleNet beats the best in class cataract detection approaches and can assist doctors in real-time cataract disease detection applications./p>