## 1 Introduction
Additionally, noting that the combination of medical images can often lead to additional clinical information that does not appear in the separate images. The objective of this review article is to highlight the interest shown in multimodal fusion by researchers in neuroimaging for the diagnosis of brain disorders, in particular AD. It consists of summarizing and examining the main applications, results, perspectives as well as the advantages and disadvantages of different MRI neuroimaging technologies for the diagnosis of brain disorders, with emphasis on the application of multimodal fusion, especially for the diagnosis of AD. Firstly this review study concerns the collection of several works related to CAD systems for the diagnosis of cerebral dementia, notably AD. We analyze their proposals which were introduced with the hope of helping the radiologist to properly assess the extent of the disease, by providing him with a second opinion in the form of a computer output. In order to reduce the rate of false negativesand improve the accuracy of the diagnosis, the researchers developed techniques notably in the two main phases in the CAD system, namely segmentation of the brain regions and classification. In this context, we inspect these techniques from artificial intelligence by highlighting their advantages and disadvantages. In addition, we identify the various measurement parameters that have been used to quantitatively assess the performance of the proposed CAD systems. For the classification process, several measurements were considered, such as the sensitivity (SE), which represents the true positive rate; specificity (SP), which estimates the true negative rate; and accuracy (AC), which determines the proportion of true results in the database, whether true positive or true negative. For the same purpose, for some work, the area under the ROC curve (AUC) value was estimated, which determines the diagnostic validity by combining sensitivity and specificity. Likewise, in the segmentation process various measures have been identified, which in the majority take into account the portions of the rates of true and false positives as well as true and false negatives, such as the Tanimoto and dice coefficients, the Jaccard similarity index, etc. Then, secondly, after identification of the gaps raised by mono-modal MRI CAD systems, we determine one of the solutions proposed in the literature within the framework of the use of data fusion techniques which can be applied in a multimodal imaging environment. The effectiveness of this approach is linked to its power to reconstruct and predict missing information from the MRI. Therefore, the most used fusion techniques are described, highlighting their advantages and disadvantages. Likewise, some key works in the literature are described, which have used multimodal fusion to improve the performance of conventional CAD systems. In addition, a performance evaluation and comparison with single-modal systems is proposed by applying reliability estimation methods such as cross-validation. The measurement parameters are also determined for the purpose of quantitatively evaluating the effectiveness of multimodal CAD systems. Finally, a discussion is tackled which highlights the interest of combining several techniques in the framework of hybrid models and the contribution of multimodal fusion and its usefulness in clinical studies. The rest of the article is organized as follows: In Section 2, first, we describe the foundations of a CAD system, then we analyze the research work already carried out using the MRI mono modality. In this regard, the methods used in the different phases of the CAD system, including the classification and segmentation of brain regions, are described, summarizing their disadvantages and advantages. We then clarify the problem addressed by the works examined by reporting the solution proposed in the literature. Consequently, the efforts investigated to find a solution within the framework of multimodal fusion of brain images are presented in Section 3.