Abstract
Glaucoma is a chronic disease resulting in vision loss characterized by gradual damage to the optic nerve. To prevent vision loss, early detection is the key solution that limits this disease. Deep learning algorithms, especially convolutional neural networks (CNNs), have recently demonstrated high robustness in medical image classification tasks. Nevertheless, to achieve this performance, CNNs need to fix the parameters before the training phase. In this paper, we investigate the impact of the batch size on the five fine-tuned pre-trained models for glaucoma detection using fundus images. Our proposal consists of finding the optimal batch size for each model, referred to by OBS. Moreover, to further enhance the performance, we have combined the models using the majority voting method, taking into account the OBS of each one. The results of five challenging datasets show that the ensemble model technique improves the performance of single-use models and outperforms similar state-of-the-arts.
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The authors would like to thank the Agency for Research Results Valuation and Technological Development, Algeria.
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Haouli, IE., Hariri, W., Seridi-Bouchelaghem, H. (2023). Glaucoma Detection Using Optimal Batch Size for Transfer Learning and Ensemble Model Techniques. In: Laouar, M.R., Balas, V.E., Lejdel, B., Eom, S., Boudia, M.A. (eds) 12th International Conference on Information Systems and Advanced Technologies “ICISAT 2022”. ICISAT 2022. Lecture Notes in Networks and Systems, vol 624. Springer, Cham. https://doi.org/10.1007/978-3-031-25344-7_19
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DOI: https://doi.org/10.1007/978-3-031-25344-7_19
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