RegionInpaint, Cutoff and RegionMix: Introducing Novel Augmentation Techniques for Enhancing the Generalization of Brain Tumor Identification

Abstract

Brain tumors are considered one of the most crucial and threatening diseases in the world as they affect the central nervous system and the main functionalities of the brain. Early diagnosis and identification of brain tumors can significantly enhance the likelihood of patient survival. Generally, deep neural networks require large samples of annotated data to achieve promising results. Most studies in the medical domain suffer from limited data which negatively impacts the model performance. Common ways to handle such problems are to generate new samples using basic augmentation techniques, generative adversarial networks, etc. In this study, we propose several novel augmentation techniques, named RegionInpaint augmentation, Cutoff augmentation, and RegionMix augmentation to improve the performance of brain tumor identification and facilitate the training of deep learning models with limited samples. In addition, traditional augmentation techniques are used to extend the training samples. A pre-trained VGG19 model is experimented along with the proposed augmentation techniques and achieved an accuracy of 100% on the unseen validation set of the SPMRI small dataset using RegionInpaint and Cutoff augmentation techniques together. On the other hand, the best testing accuracy achieved is 96.88% on the Br35H dataset which is obtained when using all the augmentation techniques together (i.e., RegionInpaint, Cutoff, RegionMix, and Basic augmentation techniques). Compared to the state-of-the-art related studies, it has been observed that our results are superior which demonstrates the efficiency of our proposed augmentation techniques and the overall proposed methodology. The source code is available at https://github.com/omarsherif200/RegionInpaint-Cutoff-and-RegionMix-augmentation-techniques.

Keywords

• Deep learning
• data augmentation
• data balancing
• image segmentation
• image classification
• image inpainting