# Alzheimer's Multiclassification Using Explainable AI Techniques
These residual blocks include shortcut connections, facilitating the direct flow of gradients through the block and preventing significant degradation. The ResNet-50 architecture consists of 50 layers, encompassing convolutional, pooling, and fully connected layers. It incorporates three main types of blocks: identity blocks, convolutional blocks with a shortcut, and the bottleneck architecture. The bottleneck architecture is particularly noteworthy for reducing computational complexity while preserving representational power. Mathematically, the residual block in ResNet-50 is expressed as \(y=F(x,W_{i})+x\), where \(x\) represents the input to the block (independent variable), \(y\) is the output (dependent variable), the residual mapping to be learned is denoted by \(F\), and \(W_{i}\) represents the block's weights (coefficients associated with \(x\)). Including a skip connection allows for the bypassing of the residual mapping, facilitating the smooth flow of gradients during the backpropagation process. In essence, the architecture employs a linear equation for each layer, contributing to the overall effectiveness of ResNet-50 in training deep neural networks. The linear equation for a single layer can be denoted as in the equation \(y=W_{1}\ x+x\). ResNet-50 has demonstrated impressive performance across various computer vision tasks, with a notable emphasis on image classification. It functions as a robust feature extractor after pre-training on extensive datasets such as ImageNet. This quality is particularly advantageous for transfer learning scenarios, especially when dealing with applications with constraints on labeled data. The architecture's depth, combined with the incorporation of skip connections, enhances its capability to capture intricate hierarchical features. These attributes collectively contribute to ResNet-50 being widely favored and integrated into cutting-edge deep learning models [14, 15]. In this study, we applied a global average pooling layer to down-sample the ResNet-50 model and calculate the average value of each feature map in the input tensor, which results in a single value feature map calculated with the following formula: \(y_{c}=\frac{1}{H^{2}\times W}\sum_{i=1}^{H}\sum_{j=1}^{W}F_{i,j,c}\), where \(F_{i,j,c}\) represents the value at position \((i,j)\) in the channel \(c\) of the feature map, \(H\) is the height as the width of the feature map is denoted by \(W\). The summation is performed over all positions in the feature map \((i,j)\). This operation reduces dimensionality before feeding the features into fully connected layers. We created a dense (fully connected) layer with three units and a softmax activation function to convert raw scores into probability distributions over multiple classes. For a given vector \(z=[z_{1},z_{2}\dots z_{k}]\) of k real numbers, the softmax function \(\sigma(z)_{i}\) for each element \(z_{i}\) is computed by \(\sigma(z)_{i}=\frac{\sigma^{z_{i}}}{\sum_{j=1}^{k}\frac{\sigma^{z}}{\sigma^{ j}}}\), where \(e\) is Euler's number, the \(i\)-th element is \(z_{i}\) (of the input vector \(z\)), and the denominator is the sum of the exponentials of all elements in the vector. This function ensures that non-negative values are returned and represent valid probabilities summing to 1. The output class is predicted based on the class with the highest probability [16]. The model was compiled to create a keras model to specify inputs from the predefined ResNet-50 model and outputs from the dense layer. The primary objectives of this study are as follows:  
* To develop a reliable and accurate classification model utilizing a deep transfer learning architecture. * To extract and represent deep features relevant for inclusion and exclusion during classification. * To enhance the interpretability of results by exploiting XAI techniques through visualization.