Many people die from lung-related diseases every year. X-ray is an effective way to test if one is diagnosed with a lung-related disease or not. This study concentrates on categorizing three distinct types of lung X-rays: those depicting healthy lungs, those showing lung opacities, and those indicative of viral pneumonia. Accurately diagnosing the disease at an early phase is critical. In this paper, five different pre-trained models will be tested on the Lung X-ray Image Dataset. SqueezeNet, VGG11, ResNet18, DenseNet, and MobileNetV2 achieved accuracies of 0.64, 0.85, 0.87, 0.88, and 0.885, respectively. MobileNetV2, as the best-performing pre-trained model, will then be further analyzed as the base model. Eventually, our own model, MobileNet-Lung based on MobileNetV2, with fine-tuning and an additional layer of attention within feature layers, was invented to tackle the lung disease classification task and achieved an accuracy of 0.933. This result is significantly improved compared with all five pre-trained models.

本文针对肺相关疾病的早期诊断问题，通过研究三种类型的肺X光图像分类，提出了一种基于MobileNetV2的定制模型MobileNet-Lung，结合了特征层的注意力机制，成功提高分类准确率至0.933。该研究填补了现有模型在肺病分类精确度上的不足，具有显著的临床应用价值。

基于迁移学习和定制CNN架构与注意力机制的肺病分类深度学习研究