التفاصيل البيبلوغرافية
| العنوان: |
Deep Learning Based Multi-Class Eye Disease Classification: Enhancing Vision Health Diagnosis. |
| المؤلفون: |
Aslam, J., Arshed, M. A., Iqbal, S., Hasnain, H. M. |
| المصدر: |
Technical Journal of University of Engineering & Technology Taxila; 2024, Vol. 29 Issue 1, p7-12, 6p |
| مصطلحات موضوعية: |
DEEP learning, NOSOLOGY, EYE diseases, CONVOLUTIONAL neural networks, DIAGNOSIS |
| مستخلص: |
Retinal abnormalities impact millions of people globally. Timely detection and treatment of these abnormalities could prevent further progression, potentially saving countless individuals from preventable blindness. However, manual disease detection is a slow, laborious process and lacks consistency in results. This study uses convolutional neural networks to categorize eye disease using a publicly available dataset. Five different pre-trained models based on convolutional neural networks (CNNs), including VGG-16, VGG-19, ResNet-50, ResNet-152, and DenseNet-121, were used in this study. We were able to detect eye diseases at the cutting edge using the refined VGG-19. With testing accuracy of 95% on the dataset, this model accurately predicted eye diseases due to the effective and same weighted precision, recall, and F1 score of 95%. The model also significantly reduces training loss while improving accuracy. [ABSTRACT FROM AUTHOR] |
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| قاعدة البيانات: |
Complementary Index |
