Abstract
Nowadays, cataracts are one of the prevalent eye conditions that may lead to vision loss. Precise and prompt recognition of the cataract is the best method to prevent/treat it in early stages. Artificial intelligence-based cataract detection systems have been considered in multiple studies. There, different deep learning algorithms have been used to recognize the disease. In this context, it has been established that the training time of the VGG19 model is very low, when compared to other Convolutional Neural Networks. Hence, in this research, the VGG19 model, for automatic cataract identification in fundus images, has been proposed for healthy lives. The performance of the VGG19 is explored with four different optimizers, i.e. Adam, AdaDelta, SGD and AdaGrad and tested on a collection of 5000 fundus images. Overall, the best experimental results reached 98% precision of classification.
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Acknowledgment
Work of Maria Ganzha is funded in part by the Centre for Priority Research Area Artificial Intelligence and Robotics of Warsaw University of Technology within the Excellence Initiative: Research University (IDUB) program.
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Kumar, R., Anand, V., Gupta, S., Ganzha, M., Paprzycki, M. (2023). Automatic Identification of Cataract by Analyzing Fundus Images Using VGG19 Model. In: Sachdeva, S., Watanobe, Y., Bhalla, S. (eds) Big Data Analytics in Astronomy, Science, and Engineering. BDA 2022. Lecture Notes in Computer Science, vol 13830. Springer, Cham. https://doi.org/10.1007/978-3-031-28350-5_11
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