Cataract Classification Based on Fundus Images Using Convolutional Neural Network

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.

Enhancing Cataract Detection Precision: A Deep Learning Approach

“…Simanjuntak et al [27] carried out an investigation to compare the effectiveness of four CNN architectures, specifically MobileNet, ResNet, GoogLeNet, and a custom CNN model, in classifying cataracts using fundus images. The results indicated that the proposed CNN model exhibited the highest level of accuracy and stability with a score of 92%, followed by MobileNet at 92%, ResNet at 93%, and GoogLeNet at 86%.…”

Optimising Cataract Detection in Fundus Images through EfficientNet-Based Classification

“…At the 20th epoch, the training accuracy rate achieved a level of 98.31%, but the validation accuracy rate stood at 96.62%. Simanjuntak et al [8] proposed CNN model achieves the highest level of accuracy (0.93) when the Adam optimizer is applied to a learning rate of 0.001. The model achieves an accuracy of 0.92 for test data and 0.93 for validation data.…”

Comparison of the Performance of Convolutional Neural Networks and Vision Transformer-Based Systems for Automated Glaucoma Detection with Eye Fundus Images

“…When this architecture is adapted to work with In medicine, the application of CNNs can be found in several fields: skin cancer [6], lung diseases [7], heart diseases [8], breast cancer [9], vascular diseases [10], etc. In the case of Ophthalmology, these CNNs have been widely used for the diagnosis of diabetic retinopathy [11], macular degeneration [12], cataracts [13], and glaucoma [14][15][16][17].…”