###### Abstract  
Recently, various deep learning methods have been applied to Modulation Format Identification (MFI). The interpretability of deep learning models is important. However, this interpretability is challenged due to the black-box nature of deep learning. To deal with this difficulty, we propose an Adaptive Mask-Based Interpretable Convolutional Neural Network (AMI-CNN) that utilizes a mask structure for feature selection during neural network training and feeds the selected features into the classifier for decision making. During training, the masks are updated dynamically with parameters to optimize feature selection. The extracted mask serves as interpretable weights, with each weight corresponding to a feature, reflecting the contribution of each feature to the model's decision. We validate the model on two datasets--Power Spectral Density (PSD) and constellation phase histogram--and compare it with three classical interpretable methods: Gradient-Weighted Class Activation Mapping (Grad-CAM), Local Interpretable Model-Agnostic Explanations (LIME), and Shapley Additive exPlanations (SHAP). The MSE values are as follows: AMI-CNN achieves the lowest MSE of 0.0246, followed by SHAP with 0.0547, LIME with 0.0775, and Grad-CAM with 0.1995. Additionally, AMI-CNN achieves the highest PG-Acc of 1, whether on PSD or on constellation phase histogram. Experimental results demonstrate that the AMI-CNN model outperforms compared methods in both qualitative and quantitative analyses. Modulation Format Identification; deep learning; interpretability; adaptive mask-based; feature selection +
Footnote †: journal: Medical Informatics  
0000-0001-5000-5000][PERSON]. [PERSON], [PERSON] *0000-00  
into the model's behavior [14]. In the complex and extensive EONs, practitioners may be familiar with optical networks but may not fully comprehend the internal algorithms of learning models. By combining optical theory with learning algorithms, practitioners can gain a better understanding of the learning model, facilitating subsequent maintenance and optimization. To enhance the interpretability of models in MFI, [PERSON]. [15] introduces a method utilizing multi-head attention mechanisms. This method enhances model interpretability by adjusting the internal structure of the model and visualizing the weights of each attention head, clearly presenting the decision-making process and key focus areas through heatmaps. However, due to the complex structure of multi-head attention mechanisms, this approach inevitably increases computational resource consumption and extends both training time and runtime. In addition, [PERSON] et al. [16] abandon high-complexity attention mechanisms in favor of the model-agnostic Grad-CAM method. They utilize Grad-CAM to generate heatmaps by computing gradient information from the final convolutional layer of a convolutional neural network. The gradient illustrates the focus areas of the model for each modulation format. This method intuitively shows the most critical regions in the input image, aiding in the understanding of the model's decision rationale. However, the Grad-CAM method, while not adding to the complexity of the model itself, requires complex gradient computations after the model training to generate heatmaps. Additionally, these studies primarily involve qualitative analysis, lacking quantitative measurement support. For instance, the color intensity of the heatmap only provides relative information about the model's focus areas and cannot precisely quantify the effectiveness of each interpretability method. To address these challenges, we introduce an Adaptive Mask-Based Interpretable Convolutional Neural Network (AMI-CNN). Our model improves discriminative capability and interpretability by using adaptive masks to highlight relevant features and suppress irrelevant ones. During training, the masks are updated dynamically with parameter adjustments to optimize feature selection.