Brain Tumor Detection and Classification on MR Images by a Deep Wavelet Auto-Encoder Model
Abstract
:1. Introduction
2. Related Work
3. Datasets
4. Methodology
- The pre-processing stage is through an enhancement filter, to improve the image; we introduce a new fusion method. In this step, the input MR brain images were resized by 256 × 256 × 1. Then, we choose a high pass filter to improve the edges of the input MR brain image. The input and output of the MR brain image are fused serially. Finally, a combined, fused MR brain image is smoothed using a 3 × 3 median filter that gives the excellent effect of segmentation results compared with previous models.
- We applied a seed-growing algorithm based on the optimal threshold for good segmentation for a brain tumor.
- In classification, we applied a deep wavelet auto-encoder (DWAE) model. In this stage, the segmented MR brain image is resized by 256 × 256 × 1 dimension for faster processing. The objective of this stage is to predict the slices with tumor (abnormal MR brain images and the slices without tumor (normal MR brain images).
4.1. Deep Wavelet Auto-Encoder
Deep Wavelet Auto-Encoder Model Training
4.2. High Pass Filter
4.3. Segmentation Using a Seeded Region Growing
4.4. Softmax Classifier
5. Experimental Results and Discussion
5.1. The Results of MR Database Processing
5.2. Performances Metrics
5.2.1. Accuracy (ACC)
5.2.2. Sensitivity (SE)
5.2.3. Specificity (SP)
5.2.4. Dice Similarity Coefficient (DSC)
5.2.5. PRECISION (PRE)
5.2.6. JACCARD Similarity Index (JSI)
5.2.7. FALSE Positive Rate (FPR)
5.2.8. FALSE Negative Rate (FNR)
Model | DSC% |
---|---|
CNN [20] | 83.7 |
CNN-small filter [49] | 88 |
CRF [55] | 62 |
HMV [45] | 85 |
3D fully connected [21] | 84.7 |
Integrated hierarchical [56] | 73 |
Local independent projection [57] | 75 |
RG + MKM + U-NET [54] | 90 |
HOG + LBP + deep features [53] | 96.11 |
Multi-scale 3D with fully connected CRF [21] | 90 |
Proposed DWAE model | 96.55 |
Model | FPR | FNR |
---|---|---|
DNN [58] | 0.16 | 0.06 |
DAE-JOA [33] | 0.46 | 0.04 |
Stacked auto-encoder [32] | 0.07 | 0.1 |
Alex-Net [52] | 0.07 | 0.128 |
Google-Net [47] | 0.714 | 0.339 |
Multimodal [59] | - | 1.74 |
KNN [48] | 0.62 | 0.54 |
Proposed DWAE model | 0.0625 | 0.031 |
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
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Model | Accuracy % | Sensitivity % | Specificity % | Precision % |
---|---|---|---|---|
DWA-DNN [29] | 93.14 | 92.16 | 94.26 | 93.15 |
DAE-JOA [33] | 98.5 | 95.4 | - | 95.6 |
CNN [46] | 96.5 | - | 95 | 94.81 |
Google-Net [47] | 89.66 | 84.85 | 96 | 96.55 |
Vgg16 [47] | 84.48 | 81.25 | 88.48 | 89.66 |
KNN [48] | 78 | 46 | 50 | 52 |
DNN [49] | 93 | 75 | 80 | 72 |
M-CNN [50] | 96.4 | 95 | 93 | 95.7 |
CNN-SVM [51] | 95.62 | - | 95 | 92.12 |
Alex-Net [52] | 87.66 | 84.38 | 92.31 | 93.1 |
HOG + LBP + Deep features [53] | 98.71 | 98.46 | 96.72 | - |
RG + MKM + U-NET [54] | 98.72 | 90.7 | 99.7 | - |
Proposed DWAE Model | 99.3 | 95.6 | 96.9 | 97.4 |
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Abd El Kader, I.; Xu, G.; Shuai, Z.; Saminu, S.; Javaid, I.; Ahmad, I.S.; Kamhi, S. Brain Tumor Detection and Classification on MR Images by a Deep Wavelet Auto-Encoder Model. Diagnostics 2021, 11, 1589. https://doi.org/10.3390/diagnostics11091589
Abd El Kader I, Xu G, Shuai Z, Saminu S, Javaid I, Ahmad IS, Kamhi S. Brain Tumor Detection and Classification on MR Images by a Deep Wavelet Auto-Encoder Model. Diagnostics. 2021; 11(9):1589. https://doi.org/10.3390/diagnostics11091589
Chicago/Turabian StyleAbd El Kader, Isselmou, Guizhi Xu, Zhang Shuai, Sani Saminu, Imran Javaid, Isah Salim Ahmad, and Souha Kamhi. 2021. "Brain Tumor Detection and Classification on MR Images by a Deep Wavelet Auto-Encoder Model" Diagnostics 11, no. 9: 1589. https://doi.org/10.3390/diagnostics11091589