Resumen
The scoliosis report is a diagnosis made by the clinician looking at X-ray images of the spine. However, with numerous images, writing the report can be time-consuming and error-prone. Therefore, this paper proposes an automatic generation model of the end-to-end scoliosis Lenke classification report. The model automatically generates a short diagnostic text to explain the results of the classifiers? Lenke classification diagnosis of scoliosis. Instead of reproducing the original diagnostic report, the original diagnostic report is described as a short sentence with diagnostic significance. In the model, the CBAM attention module is added to the residual?s path of ResNet-50 to extract key regional features of the image, and the improved Long Term and Short Term Memory Network (M-LSTM) fusion attention mechanism with additional gated operations is used as the decoder to generate more relevant description statements. The model was verified on the scoliosis dataset from Guizhou Orthopaedic Hospital, and the generated diagnostic text obtained good scores on BLEU and CIDEr evaluation indexes, and also satisfactory scores on the evaluation criteria of five professional clinicians. Therefore, the diagnostic text generated by this method had good performance in accuracy and semantic expression.