Abstract

Machine learning analysis of neuroimaging data can accurately predict chronological size/age in healthy people and deviations from healthy brain ageing have been associated with cognitive impairment and disease. In most cases, the diagnosis of brain disorders such as epilepsy or a brain tumor is slow and requires endless visits to doctors and electroencephalogram technicians. This work aims to automate brain disorder diagnosis on the basis of brain size by using Artificial Intelligence and deep learning. There are many brain disorders can be detected by reading brain size. Using the gather dataset directly from the brain with a noninvasive procedure gives significant information about its health & disease. Classifying and detecting anomalies on this brain is what doctors currently do when reading an Electroencephalography? With the right amount of dataset and the use of machine learning models, it could be possible to learn and classify brain activity like (i.e: anxiety, epilepsy spikes, abnormal tumor activity, disease etc). Subsequently, a trained Neural Network would interpret those brain datasets and identify evidence of a disorder to automate the detection and classification of those disorders / disease found.