RT info:eu-repo/semantics/article
T1 Survival and grade of the glioma prediction using transfer learning
A1 García-Olalla Olivera, Oscar
A1 Valbuena Rubio, Santiago
A1 García Ordás, María Teresa
A1 Alaiz Moretón, Héctor
A1 González Alonso, María Inmaculada
A1 Benítez Andrades, José Alberto
A2 Algebra
K1 Informática
K1 Ingeniería de sistemas
K1 Deep learning
K1 Transfer learning
K1 Convolutional neural network
K1 Glioma
K1 1203.04 Inteligencia Artificial
K1 2404 Biomatemáticas
AB [EN] Glioblastoma is a highly malignant brain tumor with a life expectancy of only 3–6 months without treatment. Detecting and predicting its survival and grade accurately are crucial. This study introduces a novel approach using transfer learning techniques. Various pre-trained networks, including EfficientNet, ResNet, VGG16, and Inception, were tested through exhaustive optimization to identify the most suitable architecture. Transfer learning was applied to fine-tune these models on a glioblastoma image dataset, aiming to achieve two objectives: survival and tumor grade prediction.The experimental results show 65% accuracy in survival prediction, classifying patients into short, medium, or long survival categories. Additionally, the prediction of tumor grade achieved an accuracy of 97%, accurately differentiating low-grade gliomas (LGG) and high-grade gliomas (HGG). The success of the approach is attributed to the effectiveness of transfer learning, surpassing the current state-of-the-art methods. In conclusion, this study presents a promising method for predicting the survival and grade of glioblastoma. Transfer learning demonstrates its potential in enhancing prediction models, particularly in scenarios with limited large datasets. These findings hold promise for improving diagnostic and treatment approaches for glioblastoma patients.
PB PeerJ Inc
LK https://hdl.handle.net/10612/18088
UL https://hdl.handle.net/10612/18088
NO Rubio, S. V., García-Ordás, M. T., Olivera, O. G. O., Alaiz-Moretón, H., González-Alonso, M. I., & Benítez-Andrades, J. A. (2023). Survival and grade of the glioma prediction using transfer learning. PeerJ Computer Science, 9, e1723.
DS BULERIA. Repositorio Institucional de la Universidad de León
RD 13-may-2024