# A Practical Multiclass Classification Network for the Diagnosis of Alzheimer's Disease
## 3 The Proposed Methodology
### Data Pre-Processing for the Proposed Network  
MRI technology has revolutionized the detection and diagnosis of neurological diseases, and researchers are continuously improving and refining MRI technology. The advantages are multi-fold, including the improved view of the tissues and non-ionizing radiation. However, deficiencies in the uniform intensity scale of the image make it difficult to visualize and evaluate the information. Moreover, the artifacts arising due to mechanical deficiencies, motion, scanner-based changes, and capturing inefficiencies must be considered before processing these images. MRI scans face several artefacts during the capturing process due to their capturing inefficiencies (i.e., wrong lens, slit position, etc.) or device limitations. These limitations contribute to the loss of the necessary information in the scans. It ultimately limits the performance of the image processing and detection frameworks [41].  
Therefore, pre-processing techniques are generally used to avoid deterioration due to enhancement in the contrast and pixel intensity. We also propose performing the prepossessing operations on the input data available for training and testing the proposed PMCAD-Net. MRI images of the brain are available as Nifti format volumes. These volumes consist of multiple slices captured during the scanning process as shown in Figure 3a. In this Figure, (b) shows the 3D views and (c) shows the axial, sagittal, and coronal views, respectively. As mentioned earlier, the direct use of this 3D volume results in several complications. In general, the physical spacing is considered a consistent parameter for the detection and segmentation tasks. The maintenance of the same resolution is the primary and desirable feature to avoid the center-specific findings. The usual image interpolation along the \(x\)-axis, \(y\)-axis, and \(z\)-axis is based on the frequent physical spacing requirement or number of voxels [42]. Thus, it is important to consider the appropriate, consistent sampling steps because non-uniformity can affect registration and resolution during subsequent processing. Image registration interprets the spatial changes with the reference image and plays a crucial role in medical image processing. Image registration in the target domain can be illustrated as slice-level registration generally performed to handle the capturing motion artifacts. It might give rise to several complications, including random noises and image interpolation [43]. However, it is out of the scope of this work. In our method, we aligned the input images of our training dataset for ease of learning for the associated classification task. Although the proposed PMCAD-Net is robust enough to learn the task without the pre-process registration step, by facilitating the training and limiting the number of false positives, we included registration for our dataset. A pre-processing step is required to convert these images into 2D images. To interpret the useable information from the available volumes, several operations are performed on the MRI scans, including skull stripping, segmentation, and spatial normalization by using SPM12 software. We segment the brain data into gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF). It is important to note that the target problem focuses on the early detection of AD and associated dementia, where we consider the GM to describe the early changes in AD. Considering the nature of the target problem, we extracted the information through brain data and regularized it at the rate of 0.0001. The data samples were extracted with the preliminary shape of 256 \(\times\) 240. These images were resized to a size of 224 \(\times\) 224 to design the dataset for the proposed PMCAD-Net.