Identity-Mapping ResFormer: A Computer-Aided Diagnosis Model for Pneumonia X-Ray Images

Tao Zhou, Caiyue Peng, Yujie Guo, Hongxia Wang, Yuxia Niu, Huiling Lu

Abstract: Pneumonia is a serious threat to human health and causes great harm to the human respiratory system. Pneumonia recognition based on lung X-ray images is an effective auxiliary diagnosis method. In the lung X-ray images, the lesion areas are complex and diverse, and the boundary is not clear. There is a gradient inconsistency problem in the feature interaction process of deep learning. They prevent the model from focusing well on diseased areas in the image. To solve these problems, this article proposes a computer-aided diagnosis model for pneumonia X-ray images—identity-mapping ResFormer. The main innovations are as follows: First, the multiconvolution cascade residual module (MCCRM) is designed to extract local image features of different sizes and shapes. The MCCRM is a “parallel-cascade” structure, which enhances the feature extraction capability of the backbone network by nesting four convolution operations. Second, the enhanced multipatch transformer (EMPT) is designed in the auxiliary network to extract the multiperceptive field’s global attention features. It enables the network to focus on prominent area features of the image. Third, the identity-mapping transformer module (IMTM) is designed to solve the gradient inconsistency problem in different stage features. Transformer operations are used in this module to fuse gradient features in different stages. Finally, the model is validated on a lung X-ray dataset. The accuracy, $F1$ , recall, precision, and specificity are 97.6679%, 95.3378%, 95.3368%, 95.4602%, and 98.4452%, respectively. Identity-mapping ResFormer can assist doctors to make efficient and accurate pneumonia diagnoses.

External IDs:dblp:journals/tim/ZhouPGWNL25