NeuroCogNet: Advanced Computational Intelligence for Neurological Diagnosis

In this article, we explore the potential of integrating machine learning (ML) into the healthcare field in the diagnosis of neurological diseases. We introduce NeuroCogNet, a cutting-edge platform that combines signal processing, artificial intelligence (AI), and ML techniques to help identify states of the brain. To support our research approach, we propose a computer-aided analysis (CAD) system that utilizes data and physiological parameters. This comprehensive review which covers two decades of research, focuses on empowering neurologists, neurosurgeons, and radiologists in their understanding of conditions such as stroke Parkinson’s disease, Alzheimer’s disease, multiple sclerosis, and ischemic brain stroke. Through an analysis of studies, we delve into various methods for extracting information from neural signals and images while also exploring techniques for reducing complexity and classifying data. The message that stands out is how CAD systems incorporating AI and advanced signal processing can significantly improve physician’s interpretation of signals and images. Some key highlights include the use of AI for analyzing arthritis with techniques for feature extraction, dimensionality reduction, and classification. We also evaluate how CAD systems have the potential to assist clinicians in making diagnoses. NeuroCogNet has emerged as a pioneer in diagnosing disorders by applying practical ML approaches to neuroscience research. These findings represent a step, in providing physicians with state-of-the-art diagnostic tools for neurologic conditions.