Revolutionizing Neurological Diagnostics: The Role of Deep Learning

<p>Neurological disorders pose a significant global health burden affecting millions&nbsp;<br/>of individuals and imposing considerable challenges on healthcare systems. Early and&nbsp;<br/>accurate diagnosis is crucial for effective management and treatment. In recent years&nbsp;<br/>deep learning methods have emerged as powerful tools for medical image analysis&nbsp;<br/>offering promising avenues for automated detection and diagnosis of neurological&nbsp;<br/>disorders. This abstract provides an overview of the current state of research in this&nbsp;<br/>field highlighting key methodologies challenges and future directions. Neurological&nbsp;<br/>disorders encompass a broad range of conditions affecting the nervous system&nbsp;<br/>including the brain spinal cord and peripheral nerves. Traditional diagnostic&nbsp;<br/>approaches often rely on clinical assessments which may be subjective and timeconsuming. The advent of deep learning techniques has revolutionized medical image&nbsp;<br/>analysis enabling the development of automated systems that can assist in the early&nbsp;<br/>and accurate detection of neurological disorders. Epilepsy is a neurological disorder&nbsp;<br/>characterized as the recurrence of two or more unprovoked seizures. The common and&nbsp;<br/>significant tool for aiding in the identification of epilepsy is electroencephalography&nbsp;<br/>(EEG).&nbsp;</p>