# Alzheimer's Multiclassification Using Explainable AI Techniques
## 3 Materials and Methods  
In this paper, the input data are sent to a deep learning model for multiclassification. The resulting predicted output is evaluated using two explainable AI techniques (Figure 1). The method we propose is based on a ResNet-50 network coupled with a channel-wise attention mechanism to perform classification. We created a local ResNet-50 model based on ImageNet weights to which we internally trained on the MRI scans. This curbs the risk of inadvertent sharing of participant-level data while preserving the analytical advantage of our approach. To assess the model's prediction, we first employ LIME using the quickshift method to highlight key features with superpixels. Our number of perturbations is 150, with the quickshift kernel size as 70, a max distance of 200 and a 0.2 ratio. We generate superpixels with this set-up and analyze the output image. Secondarily, we used GradCAM to create a superimposed image, displaying a heat map based on the final feature map from the last convolution layer. We applied channel-wise attention within Grad-CAM in order to enhance the quality of the three channels when generating the final jet heat map. To conduct the experiments in this research, we used the Alzheimer's Disease Neuroimaging Initiative (ADNI) data, which the Laboratory disseminated for Neuro Imaging at the University of Southern California. Samples can be seen in Figure 2.  
This study is based on the publicly available, large-scale ADNI dataset consisting of 10,346 sagittal brain MRI scans, categorized into three classes, as shown in Table 1: normal cognition (NC), mild cognitive impairment (MCI), and Alzheimer's disease (AD). Normal cognition is the label for patients that have no indicators of cognitive decline, such as no memory loss or motor impairments, as evidenced by the lack of pathological changes in their brain scans, while patients with mild cognitive impairment show signs of forgetfulness, which is consistent with signs of deterioration around the hippocampus. This progresses into moderate dementia, where patients begin to forget their personal history. When cognition continues to decline as the patient develops Alzheimer's disease, more historical details are forgotten, along with confusion due to neuronal damage throughout the cerebral cortex, as evidenced by the narrowing of gyri coupled with the dilation of the sulci and ventricles. Severe dementia from this point requires supervision as patients begin to forget their family members while requiring assistance for daily activities due to the development of motor symptoms; this is the stage before death according to the global deterioration scale [2; 3; 4]. Figure 1: Showing the system model and workflow. Figure 2: Raw MRI samples for normal cognition (NC) in the first row, mild cognitive impairment (MCI) in the middle row, and Alzheimer’s disease (AD) in the bottom row. These sets were then normalized through mean standardization and thereafter used to train a deep learning model using ResNet50. The positive outcome derived from the classification was utilized as input for the XAI models. The standardization is given by  
\[X_{\text{standardized}}=\frac{X-\text{Mean}(X)}{\text{Standard Deviation}(X)} \tag{1}\]  
where \(X_{\text{standardized}}\) represents the standardized value of the original variable X, Mean(X) is the average of X, and Standard Deviation(X) quantifies how much each data point differs from the dataset's average. In this study, we implemented a channel-wise self-attention mechanism to improve feature representation in a convolutional neural network. This mechanism consists of an attention block. The input tensor is initially processed through a convolutional layer with a 1 \(\times\) 1 kernel and ReLU activation to produce an intermediate feature map. This map then passes through another convolutional layer with a 1 \(\times\) 1 kernel and sigmoid activation to generate attention weights.