Pulmonary pathologies are a significant global health concern, often leading to fatal outcomes if not diagnosed and treated promptly. Chest radiography serves as a primary diagnostic tool, but the availability of experienced radiologists remains limited. Advances in Artificial Intelligence (AI) and machine learning, particularly in computer vision, offer promising solutions to address this challenge. This research evaluates a deep learning model designed to detect lung cancer, specifically pulmonary nodules, along with eight other lung pathologies, using chest radiographs. The study leverages diverse datasets comprising over 135,120 frontal chest radiographs to train a Convolutional Neural Network (CNN). A two-stage classification system, utilizing ensemble methods and transfer learning, is employed to first triage images into Normal or Abnormal categories and then identify specific pathologies, including lung nodules. The deep learning model achieves notable results in nodule classification, with a top-performing accuracy of 77%, a sensitivity of 0.713, a specificity of 0.776 during external validation, and an AUC score of 0.888. Despite these successes, some misclassifications were observed, primarily false negatives. In conclusion, the model demonstrates robust potential for generalization across diverse patient populations, attributed to the geographic diversity of the training dataset. Future work could focus on integrating ETL data distribution strategies and expanding the dataset with additional nodule-type samples to further enhance diagnostic accuracy.

本研究针对肺部疾病和肺癌的快速诊断问题，提出了一种新颖的深度学习模型，该模型利用胸部X光片检测肺结节及其他八种肺病。研究表明，该模型在肺结节分类方面实现了77%的准确率，并显示出优秀的泛化潜力，未来可通过扩展数据集和数据分布策略进一步提升诊断准确性。

快速分级CNN模型用于准确检测肺部疾病和肺癌