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September 10, 2025PLoS ONEOpen Access

A comparative study of machine learning models for automated detection and classification of retinal diseases in Ghana

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Comparative study assesses automated detection of retinal diseases using machine learning, suggesting efficacy of CNN models.

MobileNet achieved the highest accuracy at 96% and AUC of 0.975, indicating strong potential for clinical use.
DenseNet121 followed closely with 95% accuracy, underscoring its capability in retinal disease classification.
Various preprocessing techniques, including data augmentation and one-hot encoding, improved model effectiveness.
Future research should focus on expanding datasets and validating model performance in clinical environments.

Duah et al. (2025) studied this question.

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September 10, 2025PLoS ONEOpen Access

View Full Paper

Ask AI

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Authors

Comparative study assesses automated detection of retinal diseases using machine learning, suggesting efficacy of CNN models.

MobileNet achieved the highest accuracy at 96% and AUC of 0.975, indicating strong potential for clinical use.
DenseNet121 followed closely with 95% accuracy, underscoring its capability in retinal disease classification.
Various preprocessing techniques, including data augmentation and one-hot encoding, improved model effectiveness.
Future research should focus on expanding datasets and validating model performance in clinical environments.

Duah et al. (2025) studied this question.

View Full Paper

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Bookmark