2026-06-01T19:51:50Zhttps://riuma.uma.es/rest/oai/request

oai:riuma.uma.es:10630/155262026-02-03T11:54:23Zcom_10630_2254col_10630_37959

00925njm 22002777a 4500 dc Molina-Cabello, Miguel Ángel author López-Rubio, Ezequiel author Luque-Baena, Rafael Marcos author Rodríguez-Espinosa, María Jesús author Thurnhofer-Hemsi, Karl author 2018 The detection of red blood cells in blood samples can be crucial for the disease detection in its early stages. The use of image processing techniques can accelerate and improve the effectiveness and efficiency of this detection. In this work, the use of the Circle Hough transform for cell detection and artificial neural networks for their identification as a red blood cell is proposed. Specifically, the application of neural networks (MLP) as a standard classification technique with (MLP) is compared with new proposals related to deep learning such as convolutional neural networks (CNNs). The different experiments carried out reveal the high classification ratio and show promising results after the application of the CNNs. https://hdl.handle.net/10630/15526 Redes neuronales (Informática) Células sanguíneas - Clasificación Blood Cell Classification Using the Hough Transform and Convolutional Neural Networks