MRI Brain Tumor Classification Using a Hybrid VGG16-NADE Model

Sowrirajan, Saran Raj; Balasubramanian, Surendiran; Raj, Raja Soosaimarian Peter

doi:10.1590/1678-4324-2023220071

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Article - Engineering, Technology and Techniques • Braz. arch. biol. technol. 66 • 2023 • https://doi.org/10.1590/1678-4324-2023220071 copy

MRI Brain Tumor Classification Using a Hybrid VGG16-NADE Model

Authorship SCIMAGO INSTITUTIONS RANKINGS

Abstract

A brain tumour is determined to be abnormal cell development on the brain walls and inside the skull. A malignant variation is a dangerous form of cancer with an increased mortality rate. Analyzing Magnetic Resonance Imaging (MRI) through deep learning models is the most prevalent and accurate method of early cancer detection. A novel hybrid model is proposed with the VGG16 convolution neural network (CNN) and Neural Autoregressive Distribution Estimation (NADE). The experiment was conducted on 3064 MRI brain tumour images grouped into three categories. The T1 weighted contrast-enhanced MRI images were classified using the hybrid VGG16-NADE model and compared with other methods. The results prove that the proposed hybrid VGG16-NADEmodel outperforms the rest in terms of classification accuracy, specificity, sensitivity and F1 score. The prediction accuracy of the proposed hybrid VGG16-NADE is 96.01%, precision 95.72%, recall 95.64%, F-measure 95.68%, Receiver operating characteristic (ROC) 0.91, error rate 0.075, and the Matthews correlation coefficient (MCC) 0.3564. The numerical outcomes are comparatively higher than those from other approaches and it is evaluated with existing approaches like the hybrid CNN and NADE, CNN, CNN- kernel Extreme Learning Machines (KELM), deep CNN-data augmentation, and CNN- Genetic Algorithm (GA). Other metrics like the p-value, MCC, error rate and ROC are also evaluated. The experimental outcomes show that the hybrid VGG16-NADE classifier model outperforms other approaches.

Keywords:
MRI; brain tumor; VGG16; NADE model; classification; deep learning.

HIGHLIGHTS

• Proposed a hybrid VGG16_NADE model

• Additional evaluation metrics such as the macro-F1 and weighted F1 values

• Offering enhanced performance to handle problems such as identifying leaf disease and damaged paddy seeds.

Category
Meningiomas	82	708	Transverse	209
			Sagittal	231
			Coronal	268
Gliomas	89	1426	Transverse	494
			Sagittal	495
			Coronal	437
Pituitary tumours	62	930	Transverse	291
			Sagittal	320
			Coronal	319

Input	Layers	Feature mapping	Size	Kernel size	Stride	Activation
1	2^*conv	1	224^224^3	-	-	-
2	Max pool	64	224^224^64	3^*3	1	ReLU
3	2^*conv	64	224^224^64	3^*3	2	ReLU
4	Max pool	128	112^112^64	3^*3	1	ReLU
5	2^*conv	128	112^112^128	3^*3	2	ReLU
6	Max pool	256	56^56^128	3^*3	1	ReLU
7	3^*conv	256	28^28^256	3^*3	2	ReLU
8	Max pool	512	28^28^512	3^*3	1	ReLU
9	3^*conv	512	14^14^512	3^*3	2	ReLU
10	Max pool	512	14^14^512	3^*3	1	ReLU
11	FC	512	7^7^512	3^*3	2	ReLU
12	FC	-	25088	-	-	ReLU
13	FC	-	4096	-	-	ReLU
14	FC	-	4096	-	-	ReLU
Output	FC	-	1000	-	-	Softmax

Predict	Meningioma	Glioma	Pituitary
Meningioma	652	49	7
Glioma	53	1368	5
Pituitary	4	4	922

Macro-Sensitivity	Specificity	Macro-Precision	Macro-F1	Weighted-F1
95.64	97.90	95.72	95.68	96.01

	VGG 16	Three_Layer CNN	Proposed VGG16-NADE
Meningioma	89.01	88.07	92.02
Glioma	94.70	93.65	96.10
Pituitary	97.61	96.15	98.92

Methods	Accuracy (%)	Precision (%)	Recall (%)	F-measure (%)	ROC	Error rate	MCC
Hybrid CNN and NADE	95	94.49	94.21	94.56	0.86	0.089	0.4656
CNN	96.13	95.61	95.62	95.46	0.855	0.074	0.4534
CNN and KELM	93.6	83.4	76.5	72	0.84	1.56	0.4317
Deep CNN with data augmentation	94.58	81.2	75	77.9	0.80	1.63	0.4658
CNN and Genetic Algorithm	94.2	91.97	93.62	94.24	0.78	0.077	0.4205
U-net Architecture with CNN Model	95.89	95.55	95.52	95.53	0.89	0.077	0.4016
ED-GAN	92.35	91.66	92.33	91.66	0.76	1.35	0.4642
GAN + ConvNet	95.6	95.29	94.91	95.10	0.90	0.076	0.4156
Proposed Hybrid VGG16-NADE	96.01	95.72	95.64	95.68	0.91	0.075	0.3564

Epochs	1	2	3	4	5	6	7	8	9	10
Training accuracy	73.71	82.051	81.651	88.661	91.471	92.981	93.891	95.134	95.592	96.875
Training loss	51.61	39.084	31.812	25.677	20.977	17.667	15.221	12.555	11.661	8.654

Epochs	1	2	3	4	5	6	7	8	9	10
Validation accuracy	85.50	90.51	96.51	93.51	96.51	96.51	98.5	100	99.1	98.1
Validation Loss	32.23	24.26	8.63	15.05	8.74	6.87	4.36	1.83	2.36	2.36

Significance γ = 0.01
Comparison	p - Value
VGG - 16 and NADEx	0.0060

Dataset	ML algorithm	Accuracy (%)	Precision (%)	Recall (%)	F1-score	Time (min)	AUROC
Dataset	ML algorithm	Accuracy (%)	Precision (%)	Recall (%)	F1-score	Time (min)	AUROC	Kaggle for brain tumour classification	Hybrid CNN and NADE	92.54	0.74	0.70	0.14	0.10	92.86
CNN	87.87	0.77	0.68	0.13	0.10	92.36
CNN and KELM	88.98	0.71	0.71	0.14	0.78	93.86
Deep CNN with data augmentation	86.76	0.74	0.72	0.14	6.80	91.40
CNN and Genetic Algorithm	87.87	0.85	0.89	0.15	1.100	92.32
Standard CNN	93.14	0.90	0.92	0.16	0.06	95.89
Proposed VGG16-NADE	95.87	0.91	0.94	0.20	0.04	96.78
OpenfMRI dataset	Hybrid CNN and NADE	90.13	0.73	0.68	0.12	0.05	91.50
	CNN	91.12	0.75	0.67	0.12	0.08	91.14
	CNN and KELM	90.39	0.76	0.69	0.12	0.78	90.14
	Deep CNN with data augmentation	91.80	0.74	0.67	0.12	6.06	89.38
	CNN and Genetic Algorithm	90.13	0.70	0.71	0.12	1.62	91.97
	Standard CNN	93.15	0.91	0.92	0.20	0.04	92.56
	Proposed VGG16-NADE	95.56	0.92	0.93	0.24	0.04	93.58
T1-weighted-Contrast-Enhanced-MR Images brain-tumour Dataset	Hybrid CNN and NADE	95	94.49	94.21	94.56	1.25	86
	CNN	96.13	95.61	95.62	95.46	2.658	85.5
	CNN and KELM	93.6	83.4	76.5	72	4.56	84
	Deep CNN with data augmentation	94.58	81.2	75	77.9	3.56	80
	CNN and Genetic Algorithm	94.2	91.97	93.62	94.24	4.57	78
	Standard CNN	90.1	89.56	85	90	2.89	89
	Proposed VGG16-NADE	96.01	95.72	95.64	95.68	0.04	91

Metrics	Data Splitting Range (%)	VGG16-NADE	VGG16	Three_Layer CNN
Accuracy	30-70	72.18	68.27	68.18
	35-65	74.90	72.92	71.95
	40-60	79.46	74.80	74.25
	45-55	82.50	78.97	76.78
	50-50	86.55	81.08	80.01
	55-45	90.12	84.26	83.20
	60-50	93.15	87.97	85.18
	65-55	95.14	92.45	88.98
	70-30	96.01	93.57	92.22
Precision	30-70	69.87	69.87	67.25
	35-65	71.25	70.19	69.82
	40-60	74.52	72.68	70.21
	45-55	76.89	76.16	75.27
	50-50	79.81	81.02	81.97
	55-45	83.89	85.56	84.51
	60-50	87.19	89.87	87.97
	65-55	93.68	91.71	90.02
	70-30	95.72	94.38	91.17
Recall	30-70	68.97	67.46	69.46
	35-65	72.50	73.15	69.91
	40-60	75.25	76.45	72.34
	45-55	76.15	78.25	75.96
	50-50	78.56	81.86	80.99
	55-45	84.56	85.87	85.79
	60-50	88.98	87.89	88.78
	65-55	94.19	91.92	91.91
	70-30	95.64	93.57	90.28
F-measure	30-70	70.21	71.91	72.48
	35-65	73.68	74.65	75.31
	40-60	74.49	77.79	77.45
	45-55	77.21	80.67	81.26
	50-50	79.67	84.58	83.13
	55-45	84.72	87.91	86.03
	60-50	89.91	90.74	88.19
	65-55	93.46	92.68	92.57
	70-30	95.68	94.38	90.46

Performance Metrics	VGG16-NADE with Augmentation (proposed)	VGG16-NADE without Augmentation	Proposed method with change of kernel size 1 in 7^th, 10^th and 13^th layer in network
True Positive	2942	2829	2937
True Negative	6006	5893	6001
False Positive	122	235	127
False Negative	122	235	127
Accuracy	96.01	92.33	95.86
Precision	95.72	92.03	95.33
Recall	95.64	91.40	95.66
F-Score	95.68	91.68	95.66

Instituto de Tecnologia do Paraná - Tecpar Rua Prof. Algacyr Munhoz Mader, 3775 - CIC, 81350-010 Curitiba PR Brazil, Tel.: +55 41 3316-3052/3054, Fax: +55 41 3346-2872 - Curitiba - PR - Brazil
E-mail: babt@tecpar.br

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