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. 2022 Jun 1:12:873268.
doi: 10.3389/fonc.2022.873268. eCollection 2022.

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

Tahir Mohammad Ali  1 , Ali Nawaz  1 , Attique Ur Rehman  1   2 , Rana Zeeshan Ahmad  3 , Abdul Rehman Javed  4 , Thippa Reddy Gadekallu  5 , Chin-Ling Chen  6   7   8 , Chih-Ming Wu  9
Affiliations

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

Tahir Mohammad Ali et al. Front Oncol. .

Abstract

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.

Keywords: BRATS; MRI; UNET; VGG19; attention mechanism; brain tumor segmentation.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Overview of the sequential framework.
Figure 2
Figure 2
Proposed sequential framework for segmentation of brain tumor.
Figure 3
Figure 3
The architecture of VGG19.
Figure 4
Figure 4
Illustration of Dice Similarity Coefficient (DSC).
Figure 5
Figure 5
Qualitative results of the proposed framework.
See this image and copyright information in PMC

References

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