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https://hdl.handle.net/2183/49130 Retina-Focused Preprocessing for Enhanced Deep Learning Classification of Multiple Sclerosis from OCT Scans
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Álvarez-Rodríguez, Lorena
Cordón Ciordia, Beatriz
García-Martín, Elena
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L. Álvarez-Rodríguez, B. Cordón, E. García-Martín, J. Novo, J. de Moura, and M. Ortega, "Retina-focused preprocessing for enhanced deep learning classification of multiple sclerosis from OCT scans," in Proc. 20th Int. Conf. on Computer Aided Systems Theory (EUROCAST 2026), Extended Abstracts, A. Espino-Sánchez, G. S. de Blasio, C. R. García, J. C. Rodríguez, M. Quesada-Ojeda, and A. Quesada-Arencibia, Eds., Las Palmas de Gran Canaria, Spain, 2026.
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Abstract
[Abstract]: This work researches the impact of retina-focused preprocessing on OCT-based deep learning classification of Multiple Sclerosis (MS). Raw OCT B-scans were compared with anatomically preprocessed images, where the retina was segmented, flattened, and isolated from background structures. Experiments with several convolutional architectures showed that focusing on the retinal region enhances classification accuracy and consistency across repetitions, supporting the relevance of anatomically guided preprocessing in OCT-based neurological screening.
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Presentado en: EUROCAST 2026, 20th International Conference on Computer Aided Systems Theory Las Palmas de Gran Canaria, Spain, February 23- 27, 2026
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© 2026 IUCES Universidad de Las Palmas de Gran Canaria