Biomarkers Classification for Various Brain Disease using Artificial Intelligence Approach-A Study

Abstract

Abstract Diagnostic and prognostic markers for disease identification Furthermore impact of treatment on the illness continues to be a significant restriction in science. As a matter of fact, initial determination and guess of the sickness are hindered by a lack of suitable markers, and as a result, many lives are lost due to a variety of diseases because diagnosis occurs too late for a severe form of the illness because it is challenging to comprehend a recurrence. An illness’s medical condition can be positively identified using biomarkers. Diagnostic biomarkers may be crucial in this situation to make a precise diagnosis, identify individuals with a disease, and classify people with the same type of condition to tailor pharmacological therapies and boost therapeutic response. As a result, these biomarkers may be helpful for more than only diagnosing diseases; they may also be able to anticipate how a patient will respond to treatment. Research in the biology of disease is therefore heavily focused on finding appropriate and useful biomarkers for disease. Finding disease-specific biomarkers has recently been aided by deep learning. Brain functional connectivity (FC) changes may serve as biomarkers for forecasting several types of brain diseases. When the alterations are modest and there aren't any major structural changes overall, fMRI may be able to find abnormalities in the brain that other imaging methods can't. FMRI analytics are frequently used in various brain investigations, even clinical trials, in conjunction with deep learning models.