Brain Diseases related to tumor growth, Alzheimer’s Disease, mental disorders, brain cancer etc. are nowadays prevalent in today’s young as well as old people. A lot of research studies are focused on this direction associated with the brain. Modeling the brain in order to find relevant and vital features associated with such disorders is the objective of this work. In the given figure below, a database of MRI Images is utilized for the study related to brain disorders.
Initially, the input extracted from the MRI repository is passed on to Image Pre-processing module. Here, relevant filtering techniques are applied to get the pre-processed data ready for the next module, which is Feature Selection. Attribute or feature selection is a vital step in this regard to get the correct set of whole features associated with brain study in this regard. Then from the feature set, only vital features are selected so as to get rid of the redundant features. This in turn helps in dimensionality reduction of the features associated with brain data. Once the reduced set of core features, segmentation is performed to obtain the correct infected or anomalous area in which regard study is happening. The next module can be performed is prediction of disease using Machine Learning or Deep Learning Approach. Similarly, a study regarding statistical analysis can be followed to ensure and confirm the statistical measures associated in this regard. Once the model classifier is built, any test data can be checked for any anomalous occurrence or events related with brain study.
