Cataract Classification Based on Fundus Images Using Convolutional Neural Network

Richard Bina Jadi Simanjuntak - School of Electrical Engineering, Telkom University, Bandung, 40257, Indonesia
Yunendah Fu’adah - School of Electrical Engineering, Telkom University, Bandung, 40257, Indonesia
Rita Magdalena - School of Electrical Engineering, Telkom University, Bandung, 40257, Indonesia
Sofia Saidah - School of Electrical Engineering, Telkom University, Bandung, 40257, Indonesia
Abel Bima Wiratama - School of Electrical Engineering, Telkom University, Bandung, 40257, Indonesia
Ibnu Da’wan Salim Ubaidah - School of Electrical Engineering, Telkom University, Bandung, 40257, Indonesia


Citation Format:



DOI: http://dx.doi.org/10.30630/joiv.6.1.856

Abstract


A cataract is a disease that attacks the eye's lens and makes it difficult to see. Cataracts can occur due to hydration of the lens (addition of fluid) or denaturation of proteins in the lens. Cataracts that are not treated properly can lead to blindness. Therefore, early detection needs to be done to provide appropriate treatment according to the level of cataracts experienced. In this study, a comparison of cataract classification based on fundus images using GoogleNet, MobileNet, ResNet, and the proposed Convolutional Neural Network was carried out. We compared four CNN architectures when implementing the Adam optimizer with a learning rate of 0.001. The data used are 399 datasets and augmented to 3200 data. This test's best and most stable results were obtained from the proposed CNN model with 92% accuracy, followed by MobileNet at 92%, ResNet at 93%, and GoogLeNet at 86%. We also make comparisons with previous research. Most of the previous studies only used two to three class categories. In this study, the system was improved by increasing system classifies into four categories: Normal, Immature, Mature, and Hypermature. In addition, the accuracy obtained is also quite good compared to previous studies using manual feature extraction. This study is expected to help medical staff to carry out early detection of cataracts to prevent the dangerous effect of cataracts and appropriate medical treatment. In the future, we want to expand the number of datasets to improve the classification accuracy of the cataract detection system.

Keywords


Cataract; Convolutional Neural Network; GoogLeNet; MobileNet; ResNet.

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