Survival and grade of the glioma prediction using transfer learning
Santiago Valbuena Rubio María Teresa García-Ordás Oscar García-Olalla Olivera Héctor Alaiz-Moretón Maria-Inmaculada González-Alonso José Alberto Benítez-Andrades
Glioblastoma is a highly malignant brain tumor with a life expectancy of only
3 to 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.