Brain Tumor Classification in MRI Scans Using Edge Computing and a Shallow Attention-Guided CNN
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View abstract on PubMed
Summary
This summary is machine-generated.This study introduces an efficient attention-guided deep learning model for brain tumor classification using MRI scans, achieving high accuracy with reduced model size for practical medical applications.
Area Of Science
- Medical Imaging
- Artificial Intelligence
- Computational Biology
Background
- Brain tumors necessitate accurate Magnetic Resonance Imaging (MRI) for diagnosis and treatment.
- Deep learning (AI) shows promise in MRI analysis but often results in large, slow models unsuitable for edge computing.
- Efficient AI models are crucial for practical clinical deployment in brain tumor classification.
Purpose Of The Study
- To develop a novel, lightweight attention-guided classification model for brain tumors.
- To investigate methods for reducing model parameters without compromising diagnostic accuracy.
- To enhance the practicality of AI-driven brain tumor analysis for medical edge computing.
Main Methods
- A shallow attention-guided convolutional neural network (ANSA_Ensemble) was developed.
- Model compression techniques and Monte Carlo simulations were employed for evaluation.
- The model was validated on three diverse, open-source brain tumor datasets.
Main Results
- The ANSA_Ensemble model achieved high accuracies: 98.04% (best) and 96.69% (average) on the Cheng dataset.
- Comparable results were obtained on the Bhuvaji (95.16%) and Sherif (95.20%) datasets.
- Depthwise separable convolutions offered the optimal balance between accuracy and speed.
Conclusions
- The proposed attention-guided model demonstrates performance on par with state-of-the-art methods.
- Increasing attention blocks consistently improved model accuracy.
- The study provides an efficient AI solution for brain tumor classification, with publicly available code.
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