BrainAid: Efficient Brain Tumor Detection Using Lightweight CNNs and Explainable AI for Resource-Constrained Environments
Authors: Sai Sriram Gonthina, Ross Gore, Bikash Chandra Singh, Pronaya Bhattacharya, Sachin Shetty, Abhishek Phadke, Sandip Roy
Organizations: Department of Computer Science and Engineering, IIIT - Naya Raipur, Raipur, Chhattisgarh, India · Center for Secure & Intelligent Critical Systems, Old Dominion University, Virginia, USA · Department of Computer Science, California State University, Fresno, USA · Department of Computer Science and Engineering, Amity School of Engineering and Technology, Amity University, Kolkata, India · School of Engineering & Computing, Christopher Newport University, Virginia, USA
A brain tumor is an unusual growth of cells in the brain that can be cancerous or not, affecting normal brain function and causing serious risk to health. Existing solutions for brain tumor classification, such as expert manual evaluations of magnetic resonance imaging (MRI) scans, are generally inconsistent as different professionals have different criteria and can be resource-intensive, leading to delayed diagnoses. Additionally, while deep learning models have demonstrated potential, they typically require significant computational resources, motivating the need for more efficient and accessible diagnostic tools that can be deployed in real-time healthcare settings. To address these limitations, this study presents BrainAid, which makes efficient use of Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) for effective classification of four-class classification of glioma, meningioma, pituitary tumor, and no-tumor MRI scans, outperforming all the standard models such as VGG16, Inception-V3, ResNet-50 and so, while integrating Explainable AI (XAI) to improve interpretability. The BrainAid is trained to classify tumors with a high precision of 98%, achieving an F1 score of 98% and an accuracy of 98% using publicly available dataset. The model is deployed on a Raspberry Pi, demonstrating efficiency in low-resource environments. This system shows the feasibility of real-time, edge-based tumor classification, providing quick diagnostic support for medical professionals and enhancing patient care in resource-limited healthcare facilities and underserved areas.
Figures & tables
Fig. 1: Explainability Challenges: In Grad-CAM visualization, red represents highly activated regions, green represents less activation, and blue represents least focused regions.
Scheme
Year
Concept Applied
Major Contributions
Drawbacks
Anantharajan
2024
* Adaptive Contrast Enhancement
* Algorithm for noise reduction and contrast
* Does not incorporate XAI techniques
et al. [ 17 ]
Algorithm
enhancement of MRI scans
* Need a significant quantity of training
* Gray-Level Co-Occurrence Matrix
* Integrates strength of DNN and SVM
data to function accurately [ 18 ]
* Deep Neural Support Vector Machine
Hossain
2024
* Ensemble Learning
* Included model explainability
* Increased computational cost due to usage
et al. [ 19 ]
* Transfer Learning
* Achieved an accuracy of 96.94%
of multiple models
TABLE I: A comparative summary of recent schemes in Brain Tumor identification
Fig. 2: Schematic Workflow: The proposed BrainAid framework, which is primarily composed of three key constituent blocks: (i) the preprocessing block, (ii) the architecture block, and (iii) the interface block.
Fig. 3: BrainAid CNN Architecture: It is a combination of convolutional 2D, flatten, max pooling, dense, and dropout layers.
Proposed Model
Parameters
Settings
CNN
Input Size
150×150×3
Initial Learning Rate
0.001
Batch Size
32
Number of Train Batches
152
Learning Algorithm
Adam
Loss Function
Cross Entropy
TABLE II: Description of Training Configuration of the developed BrainAid CNN Model
Fig. 4: BrainAid ViT Architecture: Developed ViT model consists of patch splitting, flattening, embedding followed by transformer encoder for feature extraction task and MLP head for classification of tumors.
Model
Metric
Glioma
Meningioma
Pituitary
No Tumor
Macro Avg.
Weighted Avg.
AUC
ResNet-50
Precision
1.00
0.91
0.91
0.99
0.95
0.95
0.998
Recall
0.94
1.00
1.00
0.82
0.94
0.94
F1 Score
0.97
0.95
0.95
0.89
0.94
0.94
VGG16
Precision
0.85
0.87
0.84
0.88
0.86
0.86
0.974
Recall
0.98
0.98
0.88
0.55
0.85
0.86
F1 Score
0.91
0.92
0.86
0.67
0.84
0.85
TABLE III: Quantitative Results of Compared Methods in Terms of precision , recall, F1 score , Classwise Accuracy, corresponding Weighted Accuracy, and Area Under Curve, in which the Best ones are highlighted in BLUE
Fig. 5: Confusion Matrix: A summary of predictions by the developed BrainAid system.
Fig. 6: Model Development: The behavior of the BrainAid in loss over the range of 50 epochs.
Fig. 7: Model Development: The behavior of the BrainAid in accuracy over the range of 50 epochs.
Model
Metric
Glioma
Meningioma
Pituitary
No Tumor
Macro Avg.
Weighted Avg.
SWIN [ 19 ]
Precision
0.80
0.40
0.65
0.28
0.53
0.56
Recall
0.13
0.23
0.67
0.92
0.49
0.43
F1 Score
0.22
0.30
0.66
0.43
0.40
0.39
CCT [ 19 ]
Precision
0.74
0.76
0.78
0.26
0.63
0.68
Recall
0.15
0.60
0.43
0.96
0.53
0.48
F1 Score
0.25
0.67
0.56
0.41
0.47
0.48
TABLE IV: Quantitative Results of Compared ViT Methods in Terms of Precision, Recall, F1 Score, Classwise Accuracy, and corresponding Weighted Accuracy in which the Best ones are highlighted in BLUE and also shaded
Fig. 8: Explainability: this displays the comparison of the key areas targeted by different models, while the developed BrainAid system concentrated on better regions, demonstrating better interpretability.
Method
F1 Score
Accuracy
Number of
(%)
(%)
Classes
W. Mohamed et al. [ 30 ]
98.3
98.0
2
K. Neamah et al. [ 23 ]
99.91
99.02
2
R. Preetha et al. [ 24 ]
99.34
99.33
2
T. K. Behera et al. [ 21 ]
97.0
98
2
A. Sekhar et al. [ 27 ]
97.24
98.3
3
TABLE V: Comparison of Brain Tumor Classification Performance With Existing Methods
Fig. 9: Real-time Deployment: The user selects the MRI scan, the framework then predicts the type of tumor along with the probability score.
Model
Params
Model Size
Inference Time
(Million)
(MB)
(s)
ResNet-50
23
90
3.47
VGG16
14
56
0.94
Inception-V3
21.8
83
5.69
EfficientNet-B0
4
15
4.93
MobileNetV2
2
8
0.06
TABLE VI: Model Comparison on Parameters, Size, and Inference Time
Department of Computer Science and Engineering, University of North Texas, USA · Department of Electrical and Electronic Engineering, International Islamic University Chittagong, Bangladesh