RT Conference Proceedings
T1 Blood Cell Classification Using the Hough Transform and Convolutional Neural Networks
A1 Molina-Cabello, Miguel Ángel
A1 López-Rubio, Ezequiel
A1 Luque-Baena, Rafael Marcos
A1 Rodríguez-Espinosa, María Jesús
A1 Thurnhofer-Hemsi, Karl
K1 Redes neuronales (Informática)
K1 Células sanguíneas - Clasificación
AB The detection of red blood cells in blood samples can be crucial for the disease detection in its early stages. The use of imageprocessing 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.
PB Springer
YR 2018
FD 2018
LK https://hdl.handle.net/10630/15526
UL https://hdl.handle.net/10630/15526
LA eng
NO https://doi.org/10.1007/978-3-319-77712-2_62
NO Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech.
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 19 ene 2026