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.