## 3 Results
### XAI Functions
#### 3.6.1 Visualization  
In classification tasks, visualization provided insight into model behavior realized through backpropagation, perturbation, optimization, or feature attribution approaches. Backpropagation-based approaches include saliency maps to visually represent the importance of different areas of the input image in the prediction process. These maps highlight the pixels with the highest impact on the predicted class output. These approaches provide model-specific, local-scope, post hoc explanations. [PERSON] et al. (2022) and [PERSON] et al. (2024) used class activation mapping (CAM), which replaces the fully connected layers at the end of CNNs with global average pooling layers to spatially aggregate the feature maps from the last convolutional layer (39; 50). The layer outputs are used to create a heat map, by weighting the class-specific spatial grid with the output weights connected to the class predictions. This heat map can thereafter be overlaid on the original image to show the discriminative regions used by the network to identify that class. A generalized, gradient-weighted form of CAM, Grad-CAM, is used for classification tasks in six studies (37; 41; 47; 49; 53). Grad-CAM is model-specific to CNNs and can be used without global average pooling, allowing the application to a wider variety of CNN  
Figure 8: Frequency of identified XAI functions in the included studies, categorized by image analysis task. Note that some XAI methods served multiple functions and were used in multiple studies; hence, the total counts in this figure exceed the number of studies and XAI methods listed previously. Figure 7: Division of XAI method functions. architectures. Grad-CAM first computes the gradients of the target output concerning the feature maps of the last convolutional layer, which are global-average-pooled to obtain the importance weights for each feature map. These weights are multiplied with the forward activation maps to create the localization map and passed through a ReLU function to preserve the features that positively influence the class of interest. The result is an overlay of the produced heat map highlighting the most discriminative areas on the input image; see an example in Figure 9.  
Perturbation-based approaches perturb the input image to assess the importance of image areas for the proposed task (e.g., classification). Local interpretable model-agnostic explanations (LIME) are used in [PERSON] et al. (2022) [42]. LIME provides a local explanation by replacing a complex model locally with simpler models, for example, by approximating a CNN by a linear model; see Figure 10. The input data are perturbed and the simplified model is used to learn the change in output. The similarity of the perturbed input to the original input is used as a weight, to ensure that explanations provided by the simple models with highly perturbed inputs have less effect on the final explanation. The simple model is trained on the dataset of perturbed samples using the prediction labels and the assigned weights. The simple model's coefficients serve as the explanation for the instance. In the case of classification tasks, the coefficients reveal the contribution of each feature to the prediction of a specific class. Unlike CAM and Grad-CAM, LIME is model-agnostic and can be applied to any type of (ensemble) model. Optimization-based approaches involve optimizing the input to see what features the network learns to recognize as belonging to a certain class, rather than observing changes in output due to perturbations or backpropagation of error. [PERSON] et al. (2024) proposed activation maximization, a technique where the model's parameters are adjusted to maximize the output activation of a specific class or feature [50]. The input image is iteratively adjusted until the pattern that most excites a particular neuron or layer is found. Activation maximizations allow the exploration of what each part of a neural network is looking for in its input.