Lung x-ray image classification based on densenet and VGG feature fusion

In recent times, artificial intelligence has experienced rapid advancement and is finding its applications across diverse sectors. Within the medical domain, lung X-rays play a pivotal role in diagnosing lung ailments. Considering this, we introduce a classification algorithm tailored for lung X-ray images. This algorithm merges the strengths of DenseNet and VGG through feature fusion. To further enhance deep feature extraction, we've incorporated two attention mechanisms: the global attention block and the category attention block. Through rigorous five-fold cross-validation, it was observed that a single model could achieve a maximum accuracy of approximately 0.843. By integrating the pertinent features from both DenseNet and VGG, we noted a marginal enhancement in the overall performance. Remarkably, the introduction of the attention mechanisms onto this integrated model elevated the accuracy to 0.873. This refined model demonstrates superior diagnostic precision for 14 distinct disease types compared to other models.