A Sequential Machine Learning-cum-Attention Mechanism for Effective Segmentation of Brain Tumor

Magnetic resonance imaging is the most generally utilized imaging methodology that permits radiologists to look inside the cerebrum using radio waves and magnets for tumor identification. However, it is tedious and complex to identify the tumorous and nontumorous regions due to the complexity in the tumorous region. Therefore, reliable and automatic segmentation and prediction are necessary for the segmentation of brain tumors. This paper proposes a reliable and efficient neural network variant, i.e., an attention-based convolutional neural network for brain tumor segmentation. Specifically, an encoder part of the UNET is a pre-trained VGG19 network followed by the adjacent decoder parts with an attention gate for segmentation noise induction and a denoising mechanism for avoiding overfitting. The dataset we are using for segmentation is BRATS'20, which comprises four different MRI modalities and one target mask file. The abovementioned algorithm resulted in a dice similarity coefficient of 0.83, 0.86, and 0.90 for enhancing, core, and whole tumors, respectively.

Automated summary

Magnetic resonance imaging is commonly used for brain tumor identification, but it is time-consuming and complex. This paper proposes an attention-based convolutional neural network for brain tumor segmentation, using a pre-trained VGG19 network and attention gate for noise induction and denoising. The algorithm achieved good segmentation results on the BRATS'20 dataset.