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November 7, 2025PeerJ Computer ScienceOpen Access

Hyperparameter optimization of convolutional neural networks using particle swarm optimization for diabetic retinopathy detection

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Authors

NANedaa AlmansourSSShahnorbanun SahranAAAzizi Abdullah

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Overview

Randomized trial demonstrates improved diabetic retinopathy classification accuracy in pre-trained CNNs, suggesting effectiveness of PSO.

Key Points

  • The aim is to optimize hyperparameters of CNNs for enhancing the accuracy of diabetic retinopathy image classification.
  • Implemented variant particle swarm optimization methods for CNN hyperparameter tuning.
  • Utilized pre-trained models: VGG-16, DenseNet-121, and MobileNetV2.
  • Conducted experiments on the APTOS 2019 DR image benchmark dataset.
  • Achieved a classification accuracy of 97.41% using PSO techniques.
  • Reported sensitivity of 96.8% and specificity of 99.4%.
  • PSO-MobileNet outperformed other models in overall performance.

Cite This Study

Almansour et al. (2025) studied this question.

synapsesocial.com/papers/6a1d53b95a0c5c56ea04d8cbhttps://doi.org/10.7717/peerj-cs.3273
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