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MRI-based detection of multiple sclerosis using an optimized attention-based deep learning framework
Ramya Palaniappan1, R Delshi Howsalya Devi2, M Mathankumar3
1Department of Computer Science and Engineering, Madanapalle Institute of Technology & Science, Andhra Pradesh, India.
Background:
Multiple Sclerosis (MS) is a chronic neurological disorder affecting millions worldwide. Early detection is vital to prevent long-term disability. Magnetic Resonance Imaging (MRI) plays a crucial role in MS diagnosis, yet differentiating MS lesions from other brain anomalies remains a complex challenge.
Objective:
To develop and evaluate a novel deep learning framework-2DRK-MSCAN-for the early and accurate detection of MS lesions using MRI data.
Methods:
The proposed approach is validated using three publicly available MRI-based brain tumor datasets and comprises three main stages. First, Gradient Domain Guided Filtering (GDGF) is applied during pre-processing to enhance image quality. Next, an EfficientNetV2L backbone embedded within a U-shaped encoder-decoder architecture facilitates precise segmentation and rich feature extraction. Finally, classification of MS lesions is performed using the 2DRK-MSCAN model, which incorporates deep diffusion residual kernels and multiscale snake convolutional attention mechanisms to improve detection accuracy and robustness.
Results:
The proposed framework achieved 99.9% accuracy in cross-validation experiments, demonstrating its capability to distinguish MS lesions from other anomalies with high precision.
Conclusion:
The 2DRK-MSCAN framework offers a reliable and effective solution for early MS detection using MRI. While clinical validation is ongoing, the method shows promising potential for aiding timely intervention and improving patient care.
