# Automated Chest X-Ray Diagnosis Report Generation with Cross-Attention Mechanism
###### Abstract  
In the medical field, it is extremely important to use deep learning technology to automatically generate diagnostic reports for chest X-ray images. This technology provides an effective solution to the challenges faced by the medical field in processing large numbers of chest X-ray images. Especially during large-scale outbreaks of epidemics such as the new COVID-19, rapid and accurate screening and diagnosis of cases become important tasks. This study uses deep learning technology to automatically generate diagnostic reports for chest X-ray images, which significantly reduces the workload of doctors, reduces the risk of misdiagnosis and missed diagnosis, and provides technical support for improving public health emergency response capabilities. In this study, we propose an innovative network architecture to address the limitations of traditional image description networks in generating chest X-ray diagnostic reports, especially the large area deviation between abnormal and normal areas, and the lack of effective alignment of the two modalities of image and text. The convolutional block attention module (CBAM) is adopted to effectively alleviate the data bias problem through a sophisticated feature attention mechanism and improve the model's ability to recognize abnormal image areas. The cross-attention mechanism is adopted to optimize the alignment process between images and texts, ensuring the accuracy and reliability of the diagnosis report. deep learning technology; chest X-ray images; automatic generation of diagnostic reports; cross-attention; image and text alignment +
Footnote †: journal: Medicalonly effectively reduce the workload of doctors, but also help improve the accuracy and efficiency of the diagnostic process. With the rapid progress of deep learning and artificial intelligence technologies, the technology of automatically generating medical diagnostic reports has attracted increasing attention from the scientific research community. The evolution of this technology is based on a breakthrough in the field of image description, which uses deep learning models to automatically generate corresponding text descriptions for input images. Although this technology has achieved remarkable achievements in many fields, its application in generating medical image diagnostic reports faces more complex and challenging problems. These challenges mainly involve determining how to accurately extract and understand subtle and complex medical features in X-ray images, and how to effectively convert this visual information into accurate and practical medical text information. To meet this demand, deep learning technology needs to be further optimized and innovated in terms of algorithm accuracy, model understanding ability, and information conversion efficiency to ensure that the automatically generated diagnostic reports are both scientific and accurate and of practical value. In recent years, the research field of automatic generation of X-ray diagnostic reports has witnessed significant technological progress, which stems from multiple innovations in the field of deep learning, especially in the development of decoder and encoder technology, as well as image and text improvements in alignment methods between modes. Specifically, ([PERSON] et al.) [5] fused previously generated text and image features by combining recursive patterns. This approach not only enhanced the model's attention to reporting contextual information during training, but also improved the coherence and accuracy of text generation. In addition, there are also methods to improve the effect by using pretrained large language models: ([PERSON] et al.) [6] used the GPT model instead of the traditional decoder, which significantly enhances the model's ability to handle complex language structures and improves performance. This reflects the great potential of pretrained models in improving the automatic generation of diagnostic reports. For innovation on the encoder side, ([PERSON] et al.)