Using machine learning to investigate the effects of gray and white matter differences on balanced brain activity

Machine learning (ML) is the most popular and advanced technology which is used in most of the medical fields for data analysis and predictions. Brain is the most complex organ in the human body. Current technologies and advancements in neuroimaging have made significant changes in brain area mapping and measurements that help in understanding brain health. More accurate and effective results of brain sizes are now possible because of the development of ML techniques, which are strong methods for evaluating complicated datasets. ML algorithms might detect important characteristics of brain tissue like gray matter (GM) and white matter (WM) by training the models on large MRI brain images data sets. Convolutional neural network (CNN) is the most popular and widely used machine learning model which gives more accurate results. This may have important applications in early diagnosis, personalized medicine, and targeted therapeutic approaches. In general, our study highlights the importance of variations in GM and WM volumes based on gender and how it affects the neurobiological activities of human beings using CNN model and applying various methods involved in it.