Leveraging DenseNet121 and Data Augmentation for High-Accuracy Diabetic Retinopathy Screening
Keywords:
Diabetic Retinopathy, Convolutional Neural Network, Transfer Learning, Data Augmentation, Medical Image Classification, DenseNet121.Abstract
This study aims to classify retinal fundus images to detect diabetic retinopathy using convolutional neural networks (CNNs). The research methodology involved developing a CNN based on the DenseNet121 architecture, augmented with a dense layer of 256 neurons and a 5-neuron SoftMax output layer. Two experiments were conducted: the first applied Deep Learning (DL) to an unbalanced dataset, while the second utilized Transfer Learning (TL) combined with Data Augmentation on a balanced dataset. Results showed that DL achieved an accuracy of 79.34% with a 20.04% loss, whereas TL significantly improved performance, yielding 97.78% accuracy and only 6% loss. The study concludes that Transfer Learning with dataset balancing produces a more precise and efficient diagnostic aid compared to standard Deep Learning, offering a reliable tool to support medical professionals in accelerating screening processes and prioritizing patient care.
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