Migraine Detection Using Machine Learning: A Comprehensive Study

Abstract

The rapid growth of deep learning technologies has significantly impacted medical image analysis, enabling advancements in disease detection and classification. Migraine, a prevalent and debilitating neurological disorder, is characterized by intense, one-sided headaches accompanied by symptoms such as nausea, vomiting, and heightened sensitivity to light and sound. Diagnosing migraines remains a challenge due to the reliance on subjective patient-reported symptoms and the diagnostic guidelines provided by the International Headache Society. Recent advancements in medical imaging, particularly Magnetic Resonance Imaging (MRI), offer an opportunity to enhance diagnostic accuracy by identifying brain structural changes associated with migraines. This study presents a cutting-edge approach using the VGG19 convolutional neural network to analyze MRI scans for features indicative of migraines. By automating the processes of feature extraction and classification, this method aims to improve diagnostic precision, minimize subjectivity, and support timely medical intervention. Integrating deep learning with MRI analysis offers promising prospects for advancing migraine diagnosis and enhancing patient outcomes.

Introduction

Introduction

Migraine is a chronic neurological disorder characterized by recurrent, often severe headaches, usually accompanied by nausea, vomiting, and sensitivity to light and sound. Traditional diagnostic methods are mostly subjective, relying on self-reported symptoms and clinical evaluations, which limit accuracy.

Need for Advanced Diagnostics

To overcome these limitations, MRI and AI-based technologies are being explored for objective, accurate, and efficient diagnosis of migraines. Resting-state functional MRI (fMRI) and structural MRI have revealed brain abnormalities in migraine patients, especially in regions like the anterior cingulate and prefrontal cortex.

Role of Deep Learning

Deep learning, particularly Convolutional Neural Networks (CNNs) like VGG19, offers powerful capabilities for analyzing medical images. VGG19 is particularly well-suited for migraine classification due to:

• Its deep architecture

• Automated feature extraction

• Pre-training on large datasets

Using VGG19, the system aims to classify MRI scans into migraine vs. non-migraine with higher objectivity than traditional methods.

Literature Review Highlights

Studies have shown:

• Structural changes in migraine patients' brains, especially in the optic nerve, visual cortex, and pain processing regions.

• Functional anomalies such as cortical hyperexcitability, altered blood flow, and white matter lesions.

• Machine learning models can identify subtle changes not visible in manual assessments, enhancing diagnostic precision and reducing physician workload.

• AI algorithms have successfully extracted biomarkers like cortical thinning, disrupted connectivity, and microvascular dysfunctions.

Proposed Methodology

1. Data Acquisition

• MRI data collected from public sources (e.g., OpenNeuro, Kaggle) and medical institutions.

• Includes both migraine patients (with and without aura) and healthy controls.

• Ensures ethical compliance (e.g., informed consent, data anonymization).

2. Preprocessing Steps

• Normalization: Adjust pixel intensity to a standard scale.

• Resizing: Convert MRI scans to 224×224 pixels for VGG19 input.

• Data Augmentation: Apply rotations, flips, shifts to increase data diversity.

• Histogram Equalization: Enhance contrast for better feature detection.

3. Feature Extraction & Model Training

• VGG19 is used for hierarchical feature extraction.

• ReLU activation introduces non-linearity.

• Fine-tuning of the pre-trained model is applied for medical imaging.

4. Classification

• Final output: Migraine vs. Non-Migraine

• Uses Softmax function for probability prediction.

• Cross-entropy loss guides training optimization.

• Adam optimizer used for parameter updates.

Conclusion

This study introduces a groundbreaking migraine detection system that leverages advanced deep learning technologies to enhance the diagnostic process. By utilizing MRI images processed through the VGG19 model, the system is capable of identifying subtle abnormalities that may contribute to migraines, surpassing traditional diagnostic methods. This automated approach minimizes human error and eliminates subjectivity, offering a highly accurate, non-invasive diagnostic solution. A standout feature of this system is its ability to deliver rapid and reliable diagnostic results, ensuring consistent and precise predictions. The VGG19 model, fine-tuned with migraine-specific data, demonstrates heightened sensitivity and specificity, making it a valuable tool in clinical settings. This innovation represents the synergy of artificial intelligence and cutting-edge imaging techniques, significantly influencing the quality and efficiency of healthcare delivery. However, certain challenges remain. To maximize the system\'s potential, it is crucial to develop a diverse and high-quality MRI dataset to reduce biases and improve systematic evaluation. Additionally, addressing the computational demands of deep learning in resource-constrained environments is vital for widespread adoption in healthcare. Future efforts should focus on designing optimized architectures that maintain high accuracy while reducing computational overhead and storage requirements. This proposed system marks a significant advancement in migraine diagnosis, combining speed, precision, and reliability to monitor disease progression effectively. With ongoing research and enhancements, this innovation has the potential to redefine diagnostic practices, ultimately improving patient care and management in the long term.

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Copyright

Copyright © 2025 Dr. D. Sasikala, S. Srikannan, P. Krishnakumar, P. Ramana, M. Dhanush. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.