A Deep Convolutional Neural Network for Pneumonia Detection in X-ray Images with Attention Ensemble

• DOI: 10.3390/diagnostics14040390
• Publication Date: 2024-02-12T09:47:45Z
• Authors: Qiuyu An; Wei Chen; Wei Shao
• Keywords: Pooling; Feature (linguistics)

In the domain of AI-driven healthcare, deep learning models have markedly advanced pneumonia diagnosis through X-ray image analysis, thus indicating a significant stride in efficacy medical decision systems. This paper presents novel approach utilizing convolutional neural network that effectively amalgamates strengths EfficientNetB0 and DenseNet121, it is enhanced by suite attention mechanisms for refined classification. Leveraging pre-trained models, our employs multi-head, self-attention modules meticulous feature extraction from images. The model’s integration processing efficiency are further augmented channel-attention-based fusion strategy, one complemented residual block an attention-augmented enhancement dynamic pooling strategy. Our used dataset, which comprises comprehensive collection chest images, represents both healthy individuals those affected pneumonia, serves as foundation this research. study delves into algorithms, architectural details, operational intricacies proposed model. empirical outcomes model noteworthy, with exceptional performance marked accuracy 95.19%, precision 98.38%, recall 93.84%, F1 score 96.06%, specificity 97.43%, AUC 0.9564 on test dataset. These results not only affirm high diagnostic accuracy, but also highlight its promising potential real-world clinical deployment.