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Early prevention as well as treatment of vision loss depends on the timely detection of Ocular disorders (ODs). In medical imaging, Deep learning (DL) models have shown great promise, especially for automated disease diagnosis. The feature extraction capabilities of two popular pre-trained models, VGG-16 and AlexNet, are compared in this work. According to the results VGG-16 is faster in feature extraction than AlexNet, taking 49.69 s to complete the task compared to 55.23 s for AlexNet. Key visual aspects necessary for proper illness classification are successfully gathered by both models. The analysis displays valuable knowledge about balance speed and efficiency while selecting the best model for medical image processing. The results demonstrate how DL can increase diagnostic precision and recommend more research into optimal models for practical clinical uses.

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Deep Learning–Based Feature Identification for Ocular Disease Detection Using VGG16 Model

  • 会议论文

  • 发表日期：

    2026年

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  • Gurpreet Kaur,
  • Amit Kumar Bindal,
  • Aayush Shrivastava

摘要

Early prevention as well as treatment of vision loss depends on the timely detection of Ocular disorders (ODs). In medical imaging, Deep learning (DL) models have shown great promise, especially for automated disease diagnosis. The feature extraction capabilities of two popular pre-trained models, VGG-16 and AlexNet, are compared in this work. According to the results VGG-16 is faster in feature extraction than AlexNet, taking 49.69 s to complete the task compared to 55.23 s for AlexNet. Key visual aspects necessary for proper illness classification are successfully gathered by both models. The analysis displays valuable knowledge about balance speed and efficiency while selecting the best model for medical image processing. The results demonstrate how DL can increase diagnostic precision and recommend more research into optimal models for practical clinical uses.