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.
References
[1] Kara T, Dogan A, Baykara M, Yildiz CBT, Tural IC. Evaluation of the optic nerve in migraine patients by magnetic resonance imaging histogram analysis.Malang Neurology (t and a #) Journal 2024.10:26-29.
[2] Khan, Lal. “Migraine headache (MH) classification using machine learning methods with data augmentation.” Scientific Reports vol. 14,1 5180. 2 Mar. 2024, doi:10.1038/s41598-024-55874-0
[3] Katarina Mitrovic, Andrej M.Savic, Marko Dakovic.Machine learning approach for migraine aura complexity score prediction based on MRI data, Mitrovic . (2023) 24:169.
[4] Mitrovi?, Katarina. “Migraine with aura detection and subtype classification using machine learning algorithms and morphometric magnetic resonance imaging data.” Frontiers in neurology vol. 14 1106612. 23 June. 2023, doi:10.3389/fneur.2023.1106612
[5] Swartz RH, Kern RZ. Migraine Is Associated With Magnetic Resonance Imaging White Matter Abnormalities: A Meta-analysis. Arch Neurol. 2004;61(9):1366–1368. doi:10.1001/archneur.61.9.1366
[6] (Increased MRI-based brain age in chronic migraine patients) Rafael Navarro-González, David García-Azorín, Ángel L. Guerrero-Peral, Álvaro Planchuelo-Gómez, Santiago Aja-Fernández, Rodrigo de Luis-García medRxiv 2022.11.21.22282575;
[7] JOUR, Yang, Hao, Zhang, Junran, Liu, Qihong - Wang, Yi- 2018/10/11 Multimodal MRI-based classification of migraine: using deep learning convolutional neural network 10.1186/s12938-018-0587-0
[8] Schramm S, Börner C, Reichert M, . Functional magnetic resonance imaging in migraine: A systematic review. Cephalalgia. 2023;43(2). doi:10.1177/03331024221128278
[9] Md Mahfuzur Rahman Siddiquee, Jay Shah, Catherine Chong, Simona Nikolova, Gina Dumkrieger, Baoxin Li, Teresa Wu, Todd J Schwedt, Headache classification and automatic biomarker extraction from structural MRIs using deep learning, Brain Communications, Volume 5, Issue 1, 2023, fcac311
[10] DKD MAJUMDER, D RAY - Fundamentals of Medical Image Processing using MATLAB (books .google .co .in) .2020
[11] Hongjuan Liu, Huanfen Zhou, LinxiongZong, MRI histogram texture feature analysis of the optic nerve in the patients with optic neuritis. Chin Med SciJ; 2019. 34:18-23. DOI: 10.24920/003507
[12] Batur M, Batur A, Çilingir V, . Ultrasonic elastography evaluation in optic neuritis. Semin Ophthalmol; 2018. 33:237–241. G Sharma - int . J. Eng. Manuf. (IJEM)-Algorithms using matlab to analyze the image (3) , 8-19 ,2017.
[13] J Zhang, L Tong, L Wang, N Li. Texture analysis of multiple sclerosis: A comparative study. Magnetic Resonance imaging; 2008. 26:1160-6.
[14] Agostoni E, Aliprandi A. The complications of migraine with aura. Neurol Sci. 2006;27(2):91–5.
[15] Calandre EP, Lee. H . Cognitive disturbances and regional cerebral blood flow abnormalities in migraine patients: their relationship with the clinical manifestations of the illness. Cephalalgia. 2002;22(4):291–302.
[16] Le Pira F. Memory disturbances in migraine with and without aura: a strategy problem? Cephalalgia. 2000;20(5):475–8.
[17] Lance JW, Goadsby PJ. Mechanism and management of headache. 6th ed. Boston: Butterworth-Heinemann; 1998.
[18] Yu O, Mauss Y, Zollner G,. Namer IJ. Distinct patterns of active and non-active plaques using texture analysis on brain NMR images in multiple sclerosis patients: Preliminary results. MagnReson Imaging; 1999. 1261-7
[19] Todd J. Schwedt MD, Catherine D. Chong PhD, Teresa Wu PhD, Nathan Gaw MS, Yinlin Fu BS, Jing Li PhD
Accurate Classification of Chronic Migraine via Brain Magnetic Resonance Imaging 18 June 2015 NIH K23NS070891
[20] Chong CD, Gaw N, Fu Y, Li J, Wu T, Schwedt TJ. Migraine classification using magnetic resonance imaging resting-state functional connectivity data. Cephalalgia. 2017;37(9):828-844.
[21] Chong, Catherine Daniela. “Migraine classification using magnetic resonance imaging resting-state functional connectivity data.” Cephalalgia 37 (2017): 828 - 844.
[22] Classification of Migraine Using Static Functional Connectivity Strength and Dynamic Functional Connectome Patterns: A Resting-State fMRI Study March 2023 Brain Sciences 13(4):596