Brain Tumor Classification on MRI in Light of Molecular Markers
<|reference_start|>Brain Tumor Classification on MRI in Light of Molecular Markers: In research findings, co-deletion of the 1p/19q gene is associated with clinical outcomes in low-grade gliomas. The ability to predict 1p19q status is critical for treatment planning and patient follow-up. This study aims to utilize a specially MRI-based convolutional neural network for brain cancer detection. Although public networks such as RestNet and AlexNet can effectively diagnose brain cancers using transfer learning, the model includes quite a few weights that have nothing to do with medical images. As a result, the diagnostic results are unreliable by the transfer learning model. To deal with the problem of trustworthiness, we create the model from the ground up, rather than depending on a pre-trained model. To enable flexibility, we combined convolution stacking with a dropout and full connect operation, it improved performance by reducing overfitting. During model training, we also supplement the given dataset and inject Gaussian noise. We use three--fold cross-validation to train the best selection model. Comparing InceptionV3, VGG16, and MobileNetV2 fine-tuned with pre-trained models, our model produces better results. On an validation set of 125 codeletion vs. 31 not codeletion images, the proposed network achieves 96.37\% percent F1-score, 97.46\% percent precision, and 96.34\% percent recall when classifying 1p/19q codeletion and not codeletion images.<|reference_end|>
@article{liu2024brain,
    title={Brain Tumor Classification on MRI in Light of Molecular Markers},
    author={Jun Liu, Geng Yuan, Weihao Zeng, Hao Tang, Wenbin Zhang, Xue Lin,
  XiaoLin Xu, Dong Huang and Yanzhi Wang},
    journal={Springer Nature - Book Series: Transactions on Computational
  Science & Computational Intelligence, 2022},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.19583},
    primaryClass={eess.IV cs.CV cs.LG q-bio.QM}
}