CataractAsist: Convolutional Neural Network-Based Early Detection System for Cataracts

Abstract

Cataract disease is one of the leading causes of blindness worldwide, especially in developing countries with limited access to healthcare facilities. To address this challenge, this study aims to develop an automated cataract detection system using the Convolutional Neural Network (CNN) method. This system is designed to classify eye images into three classes, namely Normal, Mature, and Immature, by utilizing the "Senile Cataract" dataset from the Kaggle platform. The research methods include image pre-processing, feature extraction using the VGG16 model through transfer learning, model training with augmentation techniques, and performance evaluation using accuracy, precision, recall, f1-score, and confusion matrix metrics. The test results show that the model is capable of achieving 95% accuracy, with the highest f1-score in the Normal class at 0.96. Confusion matrix analysis shows excellent prediction rates for all classes, although there are slight classification errors between the Immature and Mature classes. In conclusion, this CNN-based cataract detection system is proven to be effective and accurate, and has great potential to be applied in web-based healthcare services as an automatic early diagnosis tool for eye diseases.

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