Math. Comput. Appl., Volume 29, Issue 3 (June 2024) – 17 articles
Cover Story (view full-size image):
Nuclear abnormalities in avian erythrocytes have been used as biomarkers of genotoxicity in several species. Deep learning can be used in this context to improve standardization in identifying biological configurations of medical and veterinary importance. In this study, we present a deep learning model for identifying and classifying abnormal shapes in erythrocyte nuclei of the Kelp Gull. We trained convolutional neural networks (ResNet34 and ResNet50) to obtain models capable of detecting and classifying these abnormalities. The analysis was performed at three discrimination levels of classification, with broad categories subdivided into increasingly specific subcategories. The results evidenced a fast, efficient and standardized approach that could be replicated in similar contexts. View this paper
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