DaSAM: Disease and Spatial Attention Module-Based Explainable Model for Brain Tumor Detection
Abstract
:1. Introduction
2. Literature Review
2.1. Machine Learning
2.2. Deep Learning
2.3. Pre-Trained Models
2.4. Explainable Artificial Intelligence
3. Proposed Methodology
3.1. Disease Attention Module
3.2. Spatial Attention Module
3.3. Data Preprocessing and Augmentation
4. Experimental Results
4.1. Datasets and Hyperparameter Settings
4.2. Classification Results
4.3. Ablation Analysis
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Parameters | Value |
---|---|
Epochs | 100 |
Batch Size | 32 |
Epsilon | 0.1 |
Optimizer | Adam |
Learning Rate | 0.01 |
Initial Class Weights | Figshare: 0: 1.44, 1: 0.72, 2: 1.09 |
Kaggle: 0: 0.37, 1: 0.85, 2: 1.37, 3: 1.98 | |
Early Stopping | Monitor = Validation Loss, Patience = 20, Minimum Change = 0.001 |
Class | Figshare Dataset | Kaggle Dataset | ||||||
---|---|---|---|---|---|---|---|---|
P (%) | R (%) | F (%) | A (%) | P (%) | R (%) | F (%) | A (%) | |
M | 97 | 100 | 97 | 99 | 92 | 97 | 95 | 96 |
G | 100 | 96 | 98 | 98 | 94 | 96 | ||
P | 99 | 100 | 99 | 93 | 97 | 94 | ||
N | - | - | - | 94 | 94 | 95 | ||
Macro Average | 98 | 99 | 98 | 95 | 97 | 94 | ||
Weighted Average | 98 | 98 | 98 | 95 | 96 | 94 |
Classes | P (%) | R (%) | F (%) | A (%) |
---|---|---|---|---|
M | 83 | 80 | 85 | 85 |
G | 80 | 81 | 79 | |
P | 78 | 85 | 83 | |
Macro Average | 81 | 83 | 82 | |
Weighted Average | 80 | 81 | 84 |
Results of Different Learning Rates | ||||||||
---|---|---|---|---|---|---|---|---|
Learning Rate | Figshare | Kaggle | ||||||
P (%) | R (%) | F (%) | A (%) | P (%) | R (%) | F (%) | A (%) | |
0.01 | 98 | 98 | 98 | 99 | 95 | 96 | 94 | 96 |
0.001 | 92 | 94 | 95 | 96 | 92 | 90 | 93 | 91 |
0.0001 | 93 | 95 | 93 | 92 | 91 | 88 | 90 | 89 |
0.00001 | 91 | 97 | 94 | 93 | 90 | 92 | 93 | 95 |
Results of Different Optimizers | ||||||||
Optimizers | Figshare | Kaggle | ||||||
P (% | R (%) | F (%) | A (%) | P (%) | R (%) | F (%) | A (%) | |
SGD | 86 | 83 | 82 | 85 | 79 | 80 | 81 | 83 |
RMSprop | 92 | 94 | 95 | 96 | 92 | 90 | 93 | 91 |
Adam | 98 | 98 | 98 | 99 | 95 | 96 | 94 | 96 |
Adadelta | 87 | 85 | 83 | 86 | 81 | 79 | 78 | 80 |
Results of Different Split Ratio | ||||||||
Split Ratio | Figshare | Kaggle | ||||||
P (%) | R (%) | F (%) | A (%) | P (%) | R (%) | F (%) | A (%) | |
50-25-25 | 65 | 68 | 63 | 67 | 89 | 84 | 80 | 85 |
60-10-30 | 81 | 85 | 82 | 84 | 95 | 96 | 94 | 96 |
70-15-15 | 78 | 75 | 76 | 79 | 94 | 93 | 90 | 91 |
80-10-10 | 98 | 98 | 98 | 99 | 91 | 93 | 92 | 90 |
Model | Dataset | Accuracy |
---|---|---|
CNN [43] | Figshare | 96% |
Binary Dataset | 94% | |
CNN with Pre-processing [44] | Kaggle Dataset | 96% |
Transfer learning-based CNN [45] | Figshare | 94% |
Binary Dataset | 95% | |
CNN with Pre-processing [46] | Figshare | 96% |
Kaggle Dataset | 94% | |
CNN with Pre-processing [47] | Figshare | 98% |
Kaggle Dataset | 98% | |
Proposed Model | Figshare | 99% |
Kaggle Dataset | 96% |
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Tehsin, S.; Nasir, I.M.; Damaševičius, R.; Maskeliūnas, R. DaSAM: Disease and Spatial Attention Module-Based Explainable Model for Brain Tumor Detection. Big Data Cogn. Comput. 2024, 8, 97. https://doi.org/10.3390/bdcc8090097
Tehsin S, Nasir IM, Damaševičius R, Maskeliūnas R. DaSAM: Disease and Spatial Attention Module-Based Explainable Model for Brain Tumor Detection. Big Data and Cognitive Computing. 2024; 8(9):97. https://doi.org/10.3390/bdcc8090097
Chicago/Turabian StyleTehsin, Sara, Inzamam Mashood Nasir, Robertas Damaševičius, and Rytis Maskeliūnas. 2024. "DaSAM: Disease and Spatial Attention Module-Based Explainable Model for Brain Tumor Detection" Big Data and Cognitive Computing 8, no. 9: 97. https://doi.org/10.3390/bdcc8090097