Survival and grade of the glioma prediction using transfer learning
Author and article information
1IA Department, Xeridia S.L., León, León, Spain
2SECOMUCI Research Group, Escuela de Ingenierías Industrial e Informática, Universidad de León, León, Spain
3Department of Electric, Systems and Automatics Engineering, Universidad de León, León, Spain
4SALBIS Research Group, Department of Electric, Systems and Automatics Engineering, Universidad de León, León, Spain
- Subject Areas
- Bioinformatics, Computational Biology, Artificial Intelligence, Neural Networks
- Deep learning, Transfer learning, Convolutional neural network, Glioma
- Cite this article
- 2023. Survival and grade of the glioma prediction using transfer learning. PeerJ Computer Science 9:e1723 https://doi.org/10.7717/peerj-cs.1723
The authors have chosen to make the review history of this article public.
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.