A Deep Learning Based Glioma Tumour Detection Using Efficient Visual Geometry Group Convolutional Neural Networks Architecture

The detection and segmentation of tumor regions in brain Magnetic Resonance Imaging (MRI) plays important aspects for last two decades. In this paper, Efficient Visual Geometry Group Convolutional Neural Networks Architecture is proposed for brain tumor detection which consists of pre-processing module, DWT module, and Classification module along with segmentation. The scaling based data augmentation methods are used as the pre-processing technique and the data augmented scaled images are decomposed using DWT Transform. The Wavelet feature maps are constructed from these decomposed coefficients and they are classified using EVGG-CNN architecture. This proposed architecture identifies the brain image into either Glioma or healthy brain image. The effectiveness of these developed methods stated in this work is verified by applying the proposed method on the brain images from BRATS 2021 and Kaggle dataset. The performance of proposed EVGG-CNN architecture is analyzed in terms of sensitivity, specificity and accuracy.

Keywords:
Magnetic Resonance Imaging; Glioma, brain tumors; CNN; VGG; EVGG-CNN

Layer Notation	Layer name	Specifications
C1	Convolutional	512 linear filters with 1*1 stride
C2	Convolutional	1024 linear filters with 2*2 stride
P1	Pooling	2*2- Max pool window
P2	Pooling	2*2- Max pool window
C3	Convolutional	1024 linear filters with 1*1 stride
C4	Convolutional	1024 linear filters with 2*2 stride
P3	Pooling	2*2
P4	Pooling	2*2
Dense 1	Neural Networking	512
Dense 2	Neural Networking	2

Image sequences	Experimental results in %
Image sequences	Se	Sp	Acc
P1	99.5	98.7	98.9
P2	99.2	99.6	99.3
P3	98.6	99.4	99.2
P4	98.6	98.1	98.7
P5	99.7	98.2	98.4
P6	99.3	98.8	98.6
P7	99.1	98.4	98.7
P8	98.4	98.3	98.5
P9	98.2	98.4	98.9
P10	99.3	98.3	99.3
Average	98.99	98.62	98.85

Image sequences	Experimental results in %
Image sequences	Se	Sp	Acc
K1	98.3	98.4	98.7
K 2	99.1	98.7	99.1
K 3	98.4	99.4	99.4
K 4	98.8	98.6	98.7
K 5	98.3	99.3	98.6
K 6	99.5	98.6	98.6
K 7	98.3	98.4	98.4
K 8	98.6	99.2	98.7
K 9	98.7	98.7	98.5
K 10	99.2	98.8	98.6
Average	98.72	98.81	98.73

Parameters	Datasets
Parameters	PLCO	Kaggle
Se	98.99	98.72
Sp	98.62	98.81
Acc	98.85	98.73

Image sequences	BRATS dataset			Kaggle dataset
Image sequences	Se	Sp	Acc	Se	Sp	Acc
EVGG-CNN	98.99	98.62	98.85	98.72	98.81	98.73
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