A Deep Multiscale Neural Network for Accurate Neurological Disorder Detection from MRI Scans and Real-Time Web Deployment
arXiv:2606.29106v1 Announce Type: cross Abstract: Neurological disorders involve diverse pathologies of the brain and nervous system, making early and accurate detection essential. While many deep CNNs have been developed for MRI-based classification of neurological disorders, most are optimized for binary tasks and often fail to capture the multi-class features needed to distinguish subtle anatomical differences across conditions. This study proposes the Enhanced Neurological Disorder Detection Network (End-Net) for multi-class MRI classification of neurological disorders. End-Net includes 24
Advances in deep learning and computational power are enabling more sophisticated AI applications in medical imaging, coinciding with increased demand for early disease detection.
This development represents a significant step towards more accurate and multi-class neurological disorder diagnosis, potentially transforming medical practice and patient outcomes.
Neurological disorder detection can move beyond binary classification with AI to distinguish subtle differences across multiple conditions, improving diagnostic precision.
- · Healthcare sector
- · Patients with neurological disorders
- · Medical AI companies
- · Diagnostic imaging centers
- · Traditional diagnostic methods
- · Healthcare providers with outdated technology
Improved early diagnosis leads to better treatment efficacy and patient prognoses for neurological conditions.
Increased demand for advanced MRI equipment and AI-integrated diagnostic platforms in healthcare facilities.
Ethical and regulatory discussions intensify regarding AI responsibility, bias, and data privacy in medical diagnostics.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.AI