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Fine-tuning Convolutional Neural Networks: a comprehensive guide and benchmark analysis for Glaucoma Screening

Abstract : This work aimed at giving a comprehensive, indetailed and benchmark guide on the route to fine-tuning Convolutional Neural Networks (CNNs) for glaucoma screening. Transfer learning consists in a promising alternative to train CNNs from scratch, to avoid the huge data and resources requirements. After a thorough study of five state-of-the-art CNNs architectures, a complete and well-explained strategy for fine-tuning these networks is proposed, using hyperparameter grid-searching and two-phase training approach. Excellent performance is reached on model evaluation, with a 0.9772 AUROC validation rate, giving arise to reliable glaucoma diagnosis-help systems. Also, a baseline benchmark analysis is conducted, studying the models according to performance indices such as model complexity and size, AUROC density and inference time. This in-depth analysis allows a rigorous comparison between model characteristics, and is useful for giving practioners important trademarks for prospective applications and deployments.
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https://hal-upec-upem.archives-ouvertes.fr/hal-03205034
Contributor : Rostom Kachouri <>
Submitted on : Thursday, April 22, 2021 - 9:07:49 AM
Last modification on : Friday, June 18, 2021 - 4:02:02 PM
Long-term archiving on: : Friday, July 23, 2021 - 6:09:08 PM

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  • HAL Id : hal-03205034, version 1

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Amed Mvoulana, Rostom Kachouri, Mohamed Akil. Fine-tuning Convolutional Neural Networks: a comprehensive guide and benchmark analysis for Glaucoma Screening. 25th International Conference in Pattern Recognition, Jan 2021, Milano (on line), Italy. ⟨hal-03205034⟩

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