# Automated Chest X-Ray Diagnosis Report Generation with Cross-Attention Mechanism
## 3 Results
### Comparative Experiment  
Our baseline uses a combination of ResNet and CBAM modules as its core visual feature extractor. On this basis, the extracted visual features will be directly fused with the text encoding and then input into the Transformer model for processing. It is worth notingthat in this process, no form of modality alignment module was introduced to facilitate interoperability between different modalities.  
R2 GenCMN [9] creates a modal alignment module with memory function by introducing additional storage space. This module demonstrates significant modal alignment guidance capabilities when performing tasks such as generating diagnostic reports from X-ray images, effectively enhancing the model's ability to process multimodal data. MRG [5] combines convolutional neural networks (CNNs) and recurrent neural networks (RNNs) to generate natural language sentences describing the contents of images. CNN is used to extract visual features from the input image, while RNN is responsible for generating the corresponding text description. This model introduces a visual attention mechanism that allows the model to focus on specific parts of the image when generating each word.  
The experimental results shown in Table 1 provide us with empirical data to evaluate the performance of the model. Our model shows significant advantages on the BLEU-1 (BL-1) and BLEU-2 (BL-2) metrics, reaching scores of 0.456 and 0.294, respectively, which demonstrates that our model is effective in generating image descriptions. The initial stage has a strong ability to effectively capture key words of image content. Although the improvement in the BLEU-3 (BL-3) and BLEU-4 (BL-4) indicators is smaller, it still increases, indicating that the model also has certain advantages in generating longer phrases and coherent sentences. In terms of the MTR indicator, our model also demonstrated competitiveness, achieving a score of 0.196. The MTR metric takes into account word form changes, synonym replacement, and sentence structure, thus providing a more comprehensive assessment of the quality of the generated text. The ROUGE-L (RG-L) indicator also performs well, with a score of 0.364. ROUGE-L is an evaluation metric based on the longest common subsequence, which reflects the commonality between the generated text and the reference text at the sentence level. A higher RG-L score indicates that the sentences generated by our model are closer to human descriptions overall.  
In the comparative analysis, the significant improvement in visual feature extraction of ResNet with the introduction of CBAM is worth noting. Compared with the R2 GenCMN model without the CBAM module, the improvements in MTR, RG-L, and BL-1 indicators verify the effectiveness of the CBAM module in capturing key visual information. Experimental results show that connecting image and text sequences to the cross-attention module can significantly improve the model's performance in multimodal data alignment and generation quality without additional storage space. Our model leverages the potential of deep neural networks to improve accuracy on complex image captioning tasks through fine-grained visual attention guidance.