CEGNet: A Simplified Self Attention Convolutional neural network for Pneumonia Diagnosis

In the task of pneumonia recognition from chest X-ray (CXR) images, many excellent models based on convolutional neural networks have been proposed. However, the majority of these models overlook the contextual and positional information of the image, leading to difficulties in accurately identifying the location of lesions. To address these limitations, we present a novel convolutional model with a Transformer-style architecture called CEGNet, which incorporates both position attention and spatial attention mechanisms.To enable the convolutional block to capture the global contextual information of the image, we propose a new module called Convolution Transformer (CTf), which simplifies the computation of similarity score matrices in the self-attention mechanism. To emphasize the importance of image location information, we introduce an Efficient Position Attention (EPA) module. The EPA module decomposes the channels along specific directions into two 1D encodings, capturing precise position information along those directions and applying it to the original image.Additionally, we propose a lightweight spatial attention module called Gelu Spatial Attention (GSA), which uses three consecutive 3×3 convolutions and the GELU activation function to focus on relevant spatial information in the image while reducing the number of parameters. The CTf, EPA, and GSA modules complement each other in capturing different aspects of image information, thereby enhancing the model's ability to recognize pneumonia.We explore the internal structure of the CEGNet model on the COVID-19 Radiography Dataset V5 and achieve a final accuracy of 95.805%, precision of 95.814%, recall of 95.805%, and F1-Score of 95.796% on this dataset. Furthermore, we validate the CEGNet model on the ChestXRay2017 dataset, where it also achieves excellent performance.