A Comprehensive Study on Computer Aided Approaches for Multiclass Neurological Disorder Classification

Visualizing anatomical structure benefits from medical imaging and analysis. However, employing basic imaging methods is challenging and ineffective. Brain abnormalities diagnosis and classification manually is especially a tedious task. Additionally, the approach currently in use experiences inter-observer variability while interpreting images. Diagnosis must be done with extreme care because even a small human error can have serious effects. An MRI can be used as a non-invasive method to find tumors. Early disease diagnosis can be accomplished by Computer Aided Diagnosis (CAD) systems, potentially improving survival odds and reducing the requirement for MRI analysis by a specialist. CNN have demonstrated to be quite efficient at finding tumors in brain MRIs. The automated diagnosis of brain MRI images using a unique computer-aided diagnosis method is described here. In recent decades, neuroimaging, especially MRI, has played a pivotal role in advancing our understanding of brain function and the various illnesses that can impact. Accurately identifying such illnesses from acquired neuroimaging data poses significant challenges due to the similarities in disease characteristics. This study analyses and evaluates the effectiveness of deep learning methods to identify neurological diseases. This study focuses on Alzheimer's, Picks, Huntington's, Cerebral Carcinoma and Tumors.