Detection of Cataract Disease Using Convolution Neural Network with Autoencoder


  • S.B. Saleema Parvin, A. Devendran


Cataract detection, Fundus images, Convolution Neural Network, Autoencoder, Retinal diseases


Cataracts are one of the most prevalent visual conditions that people experience as they age. A cataract is when the lens of the eyes develops a fog. The major signs and symptoms of this disease are blurred vision, fading colors, and difficulties seeing in bright light. Having trouble doing a number of chores is typically the outcome of these symptoms. Therefore, early cataract identification and prevention may aid in lowering the prevalence of blindness. This study uses Convolutional Neural Networks (CNN) to categorize cataract disease using an image dataset which are freely accessible. The proposed CNN fuses the Autoencoder (AE) advantages of CNN as pre-trained to preserve better correlation between the patches. When comparing it with classical diagnosis technique, image classification by CNN is potential performance and cost efficient method.  As a result, the goal of the current study is to create a model for predicting cataracts. A human grader may find it difficult to recognize the earliest small alterations in the optic disc. The Deep Learning (DL) encoder can learn subtle characteristics in fundus images of individuals with early-stage cataracts because we have access to cataract fundus images that have been classified based on a thorough ophthalmologic examination.


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How to Cite

S.B. Saleema Parvin. (2024). Detection of Cataract Disease Using Convolution Neural Network with Autoencoder. International Journal of Intelligent Systems and Applications in Engineering, 12(21s), 2878 –. Retrieved from



Research Article