TY  - EJOU
AU  - Zeng, Zhigao 
AU  - Liu, Jun 
AU  - Zheng, Bing 
AU  - Yi, Shengqiu 
AU  - Yuan, Xinpan 
AU  - Liu, Qiang 

TI  - A Pneumonia Recognition Model Based on Multiscale Attention Improved EfficientNetV2
T2  - Computers, Materials \& Continua

PY  - 2025
VL  - 84
IS  - 1
SN  - 1546-2226

AB  - To solve the problems of complex lesion region morphology, blurred edges, and limited hardware resources for deploying the recognition model in pneumonia image recognition, an improved EfficientNetV2 pneumonia recognition model based on multiscale attention is proposed. First, the number of main module stacks of the model is reduced to avoid overfitting, while the dilated convolution is introduced in the first convolutional layer to expand the receptive field of the model; second, a redesigned improved mobile inverted bottleneck convolution (IMBConv) module is proposed, in which GSConv is introduced to enhance the model’s attention to inter-channel information, and a SimAM module is introduced to reduce the number of model parameters while guaranteeing the model’s recognition performance; finally, an improved multi-scale efficient local attention (MELA) module is proposed to ensure the model’s recognition ability for pneumonia images with complex lesion regions. The experimental results show that the improved model has a computational complexity of 1.96 GFLOPs, which is reduced by 32% relative to the baseline model, and the number of model parameters is also reduced, and achieves an accuracy of 86.67% on the triple classification task of the public dataset Chest X-ray, representing an improvement of 2.74% compared to the baseline model. The recognition accuracies of ResNet50, Inception-V4, and Swin Transformer V2 on this dataset are 84.36%, 85.98%, and 83.42%, respectively, and their computational complexities and model parameter counts are all higher than those of the proposed model. This indicates that the proposed model has very high feasibility for deployment in edge computing or mobile healthcare systems. In addition, the improved model achieved the highest accuracy of 90.98% on the four-classification public dataset compared to other models, indicating that the model has better recognition accuracy and generalization ability for pneumonia image recognition.
KW  - Pneumonia recognition; EfficientNetV2; GSConv; SimAM

DO  - 10.32604/cmc.2025.063257