DCTNet: Densely contextual transformer networks for diagnosis of cervical lesions

In recent years, few studies have been conducted on the hierarchical assisted diagnosis of cervical lesions and most of them are still insufficient and their clinical significance is extremely limited. Based on this, this paper designs densely contextual transformer networks (DCTNet) to diagnose cervical lesions. DCTNet benefits from its internal DCT module and multi-spectral channel attention (MSCA) module to diagnose cervical lesions efficiently. First, the DCT module effectively combines the ability of the transformer to capture global contextual information with the ability of the convolutional neural networks to capture neighboring local contextual information, which enhances the visual representation of the network. Second, the MSCA module can effectively use the information of more frequency components to enrich the feature representation ability of the network. The results show that under the same research conditions, the diagnostic performance of DCTNet is generally superior to other current diagnostic methods; the area under the curve value is 0.8332. While the clinical diagnostic performance reaches the level of advanced physicians, close to the expert physicians, with excellent auxiliary diagnostic performance. Therefore, DCTNet can effectively alleviate the pressure on physicians in resource-poor areas.