## 4 Conclusions and General Requirements  
Many advanced countries with a long life expectancy, such as Canada, Japan and the United States, are moving toward an aging society, and thus the number of patients with brain diseases, including some dementia disorders, will increase as AD with the rise in average life expectancy. Neuroradiologists expect that the CAD systems can assist them in diagnosing brain diseases by providing useful information. Therefore, the CAD systems for brain diseases, especially for AD which is the most common form of dementia, will become more essential for neuroradiologists in clinical practice in the near future. Moreover, because MR neuroimaging used in practice is one of the most widely used imaging modalities to establish trusted clinical settings in brain medical studies in this regard, this review provides a preliminary summary and reported some studies of researchers which attempt to develop very useful magnetic resonance CAD systems for brain disorders and AD in particular. In this context, the various machine learning techniques that have been explored for in vivo classification, and particularly segmentation phase in the CAD MR brain system, have been described and criticized, specifying the disadvantages and advantages of each of them. We are interested in machine learning methodologies because they are highly flexible to the inclusion of expert knowledge and have been demonstrated in numerous applications to perform accurately and robustly. In the context of the brain regions segmentation, despite intensive research in this area, there is currently no reliable method for all types of images. Nevertheless, a set of ad hoc methods that has received a certain degree of popularity and that we have divided into five categories are: Techniques based on the shape approach, based on the contour approach, based on the region approach, based on theory graphs and those based on the structural approach. In the context of the classification process, existing work reported in the literature has shown that the classification of brain images is possible via techniques based on supervised or unsupervised learning. However, due to the specificity of the brain images in which the CAD system must be formed, based on the field of truth or clinical evidence, the researchers preferred to apply methods based on supervised learning and rejected unsupervised classification, which does not take into account training data. However, because of the drawbacks raised by the classification and segmentation methods, it has been found that different researchers have turned to new tracks to maximize the accuracy of CAD systems. Among these tracks, the hybrid models which make it possible to combine the advantages of various techniques derived from \"soft computing\". MRI provides high-resolution images with anatomical information. On the other hand, functional images, such as PET, SPECT, etc., provide low-spatial resolution images with functional information. Therefore, a single modality of medical images cannot provide comprehensive and accurate information. As a result, combining anatomical and functional medical images to provide much more useful information through image fusion, has become the focus of imaging research and processing. Different biomarkers provide complementary information, which have been shown in the literature to be useful in neuroimaging and AD diagnoses when used together. Thus we showed in this survey paper that the combination of MRI and other modalities using data mining methods, as a tool, more accurately classifies brains disorder patients as AD subjects at the baseline compared to using either biomarker separately. For this purpose, some results of related-work were reported, and the most used image fusion methods were summarized by specifying the disadvantages and the advantages of each one. Several multimodal fusion methods have been devised by the researchers.