Abstract: Aiming at the problem that the diagnosis of pleomorphic adenoma completely relied on manual labor, we proposed a computer
-assisted diagnostic method. Firstly, by  collecting data and constructing a pleomorphic adenoma dataset, the current dense connection nertwork was improved and fused with the channel attention mechanism for disease tissue classification feature extraction to obtain tissue categories and probabilities. Secondly, by using classification and regression trees (CART), we obtained diagnostic results and  provided manual assistance in the selection of difficult categories, thus achieving  computer-assisted work on pleomorphic adenomatous diseases. The experimental results show that the method  achieves  classification extraction accuracy of 97.7% in the classification recognition module, and decision tree inference diagnostic accyracy of 100%. In addition, the accuracy of classification recognition module  achieves 98.6%  in the field of blood cell classification, and the method has certain transferability and validity.

Key words: computer application, polymorphic adenoma, dense connection, attention mechanisms, decision tree