Revolutionizing Neurological Diagnostics: The Role of Deep Learning

Neurological disorders pose a significant global health burden, affecting millions

of individuals and imposing considerable challenges on healthcare systems. Early and

accurate diagnosis is crucial for effective management and treatment. In recent years,

deep learning methods have emerged as powerful tools for medical image analysis,

offering promising avenues for automated detection and diagnosis of neurological

disorders. This abstract provides an overview of the current state of research in this

field, highlighting key methodologies, challenges, and future directions. Neurological

disorders encompass a broad range of conditions affecting the nervous system,

including the brain, spinal cord, and peripheral nerves. Traditional diagnostic

approaches often rely on clinical assessments, which may be subjective and timeconsuming. The advent of deep learning techniques has revolutionized medical image

analysis, enabling the development of automated systems that can assist in the early

and accurate detection of neurological disorders. Epilepsy is a neurological disorder

characterized as the recurrence of two or more unprovoked seizures. The common and

significant tool for aiding in the identification of epilepsy is electroencephalography

(EEG).