Summary of Survival and Grade Of the Glioma Prediction Using Transfer Learning, by Santiago Valbuena Rubio et al.
Survival and grade of the glioma prediction using transfer learning
by Santiago Valbuena Rubio, María Teresa García-Ordás, Oscar García-Olalla Olivera, Héctor Alaiz-Moretón, Maria-Inmaculada González-Alonso, José Alberto Benítez-Andrades
First submitted to arxiv on: 4 Feb 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel approach to detecting and predicting the survival and grade of glioblastoma brain tumors has been developed using transfer learning techniques. The study tested various pre-trained networks, including EfficientNet, ResNet, VGG16, and Inception, through exhaustive optimization to identify the most suitable architecture. Transfer learning was applied to fine-tune these models on a glioblastoma image dataset, achieving high accuracy in survival prediction (65%) and tumor grade prediction (97%). The study’s findings demonstrate the potential of transfer learning in enhancing prediction models, particularly in scenarios with limited large datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Glioblastoma is a very bad brain tumor that can’t be cured. Doctors need to know how long patients will live and what kind of tumor they have. This study found a new way to do this using special computer programs called neural networks. They tested different types of neural networks on pictures of glioblastomas and found the best one. The program was then trained to predict survival time and tumor grade, and it got very good results! It can accurately predict whether patients will live for a short, medium or long time, and whether their tumor is low-grade (less bad) or high-grade (very bad). This study shows that this new approach could help doctors diagnose and treat glioblastoma better. |
Keywords
* Artificial intelligence * Optimization * Resnet * Transfer learning
