2026-05-05T18:33:28Zhttps://uvadoc.uva.es/oai/request

oai:uvadoc.uva.es:10324/804442026-04-22T07:32:21Zcom_10324_1134com_10324_931com_10324_894col_10324_1213

2025-12-10T13:11:34Z urn:hdl:10324/80444 Machine Learning Representation of Loss of Eye Regularity in a Drosophila Neurodegenerative Model Diez Hermano, Sergio Ganfornina Álvarez, María Dolores Vega-Lozano, Esteban Sánchez Romero, Diego Producción Científica The fruit fly compound eye is a premier experimental system for modeling human neurodegenerative diseases. The disruption of the retinal geometry has been historically assessed using time-consuming and poorly reliable techniques such as histology or pseudopupil manual counting. Recent semiautomated quantification approaches rely either on manual region-of-interest delimitation or engineered features to estimate the extent of degeneration. This work presents a fully automated classification pipeline of bright-field images based on orientated gradient descriptors and machine learning techniques. An initial region-of-interest extraction is performed, applying morphological kernels and Euclidean distance-to-centroid thresholding. Image classification algorithms are trained on these regions (support vector machine, decision trees, random forest, and convolutional neural network), and their performance is evaluated on independent, unseen datasets. The combinations of oriented gradient C gaussian kernel Support Vector Machine [0.97 accuracy and 0.98 area under the curve (AUC)] and fine-tuned pre-trained convolutional neural network (0.98 accuracy and 0.99 AUC) yielded the best results overall. The proposed method provides a robust quantification framework that can be generalized to address the loss of regularity in biological patterns similar to the Drosophila eye surface and speeds up the processing of large sample batches. 2025-12-10T13:11:34Z 2025-12-10T13:11:34Z 2020 info:eu-repo/semantics/article Front Neurosci. 2020 Jun 4;14:516 https://uvadoc.uva.es/handle/10324/80444 10.3389/fnins.2020.00516 Frontiers in Neuroscience 14 1662-453X spa info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by-nc-nd/4.0/ Attribution-NonCommercial-NoDerivatives 4.0 Internacional