In recent years, the integration of deep learning techniques into medical imaging has revolutionized the diagnosis and treatment of lung diseases, particularly in the context of COVID-19 and pneumonia. This paper presents a novel, lightweight deep learning based segmentation-classification network designed to enhance the detection and localization of lung infections using chest X-ray images. By leveraging the power of transfer learning with pre-trained VGG-16 weights, our model achieves robust performance even with limited training data. The architecture incorporates refined skip connections within the UNet++ framework, reducing semantic gaps and improving precision in segmentation tasks. Additionally, a classification module is integrated at the end of the encoder block, enabling simultaneous classification and segmentation. This dual functionality enhances the model's versatility, providing comprehensive diagnostic insights while optimizing computational efficiency. Experimental results demonstrate that our proposed lightweight network outperforms existing methods in terms of accuracy and computational requirements, making it a viable solution for real-time and resource constrained medical imaging applications. Furthermore, the streamlined design facilitates easier hyperparameter tuning and deployment on edge devices. This work underscores the potential of advanced deep learning architectures in improving clinical outcomes through precise and efficient medical image analysis. Our model achieved remarkable results with an Intersection over Union (IoU) of 93.59% and a Dice Similarity Coefficient (DSC) of 97.61% in lung area segmentation, and an IoU of 97.67% and a DSC of 87.61% for infection region localization. Additionally, it demonstrated high accuracy of 93.86% and sensitivity of 89.55% in detecting chest diseases, highlighting its efficacy and reliability.

本研究针对肺部疾病（尤其是COVID-19和肺炎）诊断中的检测和定位不足问题，提出了一种基于轻量深度学习的分割-分类网络。通过使用预训练的VGG-16权重，研究展示了在有限训练数据下仍能实现的强大表现，最终实现了肺区域分割和感染区域定位的高准确率，具有实时和资源有限环境下的应用潜力。

InfLocNet：基于轻量深度学习的胸部X光图像增强肺部感染定位与疾病检测