Comparison of ResNet-50, EfficientNet-B1, and VGG-16 Algorithms for Cataract Eye Image Classification
DOI:
https://doi.org/10.30871/jaic.v9i2.8968Keywords:
ResNet-50, EfficientNet-B1, Classification, Eye, CataractAbstract
Cataract is a leading cause of blindness worldwide, emphasizing the need for an effective early detection approach. This study evaluates the capabilities of three widely-used deep learning models—ResNet-50, EfficientNet-B1, and VGG-16—in classifying visual data. The analysis was conducted on a dataset of 2,112 images, comprising 1,074 normal cases and 1,038 cataract cases. The findings reveal that ResNet-50 achieved the best accuracy at 98.61%, followed by EfficientNet-B1 at 96.64% and VGG-16 at 93.82%. In comparison, previous research using Convolutional Neural Network (CNN) techniques reported an accuracy of 92.93%. These results highlight ResNet-50's superior potential for image classification tasks in this domain. This study contributes significantly to the selection of robust models for building an automated cataract detection framework.
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