Brain Tumor/Mass Classification Framework Using Magnetic-Resonance-Imaging-Based Isolated and Developed Transfer Deep-Learning Model
Abstract
:1. Introduction
2. Materials and Methods
2.1. Magnetic Resonance Imaging (MRI) Dataset
2.2. Isolated and Transfer Learning
2.3. Methodology
2.3.1. Magnetic Resonance Images Pre-Processing
2.3.2. Developed Isolated and Transfer Deep-Learning Models
Isolated Convolutional Neural Network Model
Transfer Learning
Optimization
2.3.3. Proposed Framework
3. Results
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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No Tumor | Glioma Tumor | Meningioma Tumor | Pituitary Tumor | |
---|---|---|---|---|
Brain MRI Images | | | | |
Layer No. | Layer Type | Properties | Learnable |
---|---|---|---|
1 | Image Input | 227 × 227 × 3 images with ‘zerocenter’ normalization | - |
2 | Convolutional | 128 6 × 6 convolutions with stride [4 4] and padding [0 0 0 0] | Weights: 6 × 6 × 3 × 128 Bias: 1 × 1 × 128 |
3 | ReLU | ReLU | - |
4 | Cross Channel Normalization | cross channel normalization with 5 channels per element | - |
5 | Max | 2 × 2 max pooling with stride [2 2] and padding [0 0 0 0] | - |
6 | Convolutional | 96 6 × 6 convolutions with stride [1 1] and padding [2 2 2 2] | Weights: 6 × 6 × 3 × 128 × 96 Bias: 1 × 1 × 96 |
7 | ReLU | ReLU | - |
8 | Max | 2 × 2 max pooling with stride [2 2] and padding [0 0 0 0] | - |
9 | Convolutional | 96 2 × 2 convolutions with stride [1 1] and padding [2 2 2 2] | Weights: 2 × 2 × 96 × 96 Bias: 1 × 1 × 96 |
10 | ReLU | ReLU | - |
11 | Max | 2 × 2 max pooling with stride [2 2] and padding [0 0 0 0] | - |
12 | Convolutional | 24 6 × 6 convolutions with stride [1 1] and padding [2 2 2 2] | Weights: 6 × 6 × 96 × 24 Bias: 1 × 1 × 24 |
13 | ReLU | ReLU | - |
14 | Max | 2 × 2 max pooling with stride [2 2] and padding [0 0 0 0] | - |
15 | Convolutional | 24 6 × 6 convolutions with stride [1 1] and padding [2 2 2 2] | Weights: 2 × 2 × 24 × 24 Bias: 1 × 1 × 24 |
16 | ReLU | ReLU | - |
17 | Batch Normalization | Batch normalization | Offset: 1 × 1 × 24 Scale: 1 × 1 × 24 |
18 | Fully | 512 fully connected layer | Weights: 512 × 96 Bias: 512 × 1 |
19 | Dropout | 30% dropout | - |
20 | Fully | 2 fully connected layer | Weights: 2 × 512 Bias: 2 × 512 |
21 | Softmax | - | - |
22 | Classification Output | - | - |
Network | Training Accuracy (%) | Training Loss | Training Time | Validation Accuracy (%) | Validation Loss |
---|---|---|---|---|---|
19-layers | 100 | 2.8016 × 10−5 | 15 min 58 s | 98.50 | 0.0850 |
22-layers | 100 | 4.8811 × 10−6 | 16 min | 99.33 | 0.0534 |
25-layers | 100 | 4.8243 × 10−7 | 15 min 43 s | 98.33 | 0.1412 |
Network | Training Accuracy (%) | Training Loss | Training Time | Validation Accuracy (%) | Validation Loss |
---|---|---|---|---|---|
19-layers | 100 | 1.9454 × 10−4 | 14 min 57 s | 91.27 | 0.4637 |
22-layers | 100 | 2.7508 × 10−5 | 14 min 35 s | 92.67 | 0.3208 |
25-layers | 100 | 1.6904 × 10−6 | 14 min 22 s | 91.62 | 0.7276 |
Class | Classified as | TPR (%) | FNR (%) | PPV (%) | FDR (%) | Training Time | Validation Accuracy | ||
---|---|---|---|---|---|---|---|---|---|
Glioma | Meningioma | Pituitary | |||||||
Glioma | 157 | 6 | 0 | 96.32 | 3.68 | 95.15 | 4.85 | 13 min 8 s | 95.75% |
Meningioma | 8 | 151 | 0 | 94.97 | 5.03 | 92.07 | 7.93 | ||
Pituitary | 0 | 7 | 165 | 95.93 | 4.07 | 100 | 0 |
Study | Type of Dataset | Model | Accuracy (%) | Training Time |
---|---|---|---|---|
Abiwinanda et al. [21] | Dataset-III | 13-layer CNN | 84.19 | - |
Irmak. [23] | Dataset-II | 25-layer CNN | 92.66 | - |
Kang et al. [26] | Dataset-III | Pre-trained CNN models with machine-learning classifiers | 93.72 | - |
Rehman et al. [34] | Dataset-III | AlexNet GoogleNet VGGNet | 95.86 95.61 95.42 | 43 min 79 min 89 min |
Proposed | Dataset-II Dataset-III | Developed transfer-learned CNN | 95.75 96.90 | 13 min |
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Alanazi, M.F.; Ali, M.U.; Hussain, S.J.; Zafar, A.; Mohatram, M.; Irfan, M.; AlRuwaili, R.; Alruwaili, M.; Ali, N.H.; Albarrak, A.M. Brain Tumor/Mass Classification Framework Using Magnetic-Resonance-Imaging-Based Isolated and Developed Transfer Deep-Learning Model. Sensors 2022, 22, 372. https://doi.org/10.3390/s22010372
Alanazi MF, Ali MU, Hussain SJ, Zafar A, Mohatram M, Irfan M, AlRuwaili R, Alruwaili M, Ali NH, Albarrak AM. Brain Tumor/Mass Classification Framework Using Magnetic-Resonance-Imaging-Based Isolated and Developed Transfer Deep-Learning Model. Sensors. 2022; 22(1):372. https://doi.org/10.3390/s22010372
Chicago/Turabian StyleAlanazi, Muhannad Faleh, Muhammad Umair Ali, Shaik Javeed Hussain, Amad Zafar, Mohammed Mohatram, Muhammad Irfan, Raed AlRuwaili, Mubarak Alruwaili, Naif H. Ali, and Anas Mohammad Albarrak. 2022. "Brain Tumor/Mass Classification Framework Using Magnetic-Resonance-Imaging-Based Isolated and Developed Transfer Deep-Learning Model" Sensors 22, no. 1: 372. https://doi.org/10.3390/s22010372
APA StyleAlanazi, M. F., Ali, M. U., Hussain, S. J., Zafar, A., Mohatram, M., Irfan, M., AlRuwaili, R., Alruwaili, M., Ali, N. H., & Albarrak, A. M. (2022). Brain Tumor/Mass Classification Framework Using Magnetic-Resonance-Imaging-Based Isolated and Developed Transfer Deep-Learning Model. Sensors, 22(1), 372. https://doi.org/10.3390/s22010372