A CNN-Based Framework for Automatic Segmentation of Chest X-ray Images and Multi-Type Respiratory Disease Recognition

Respiratory diseases pose a significant threat to human health, and early, accurate diagnosis is critical for improving patient outcomes.Chest X-ray imaging, due to its affordability and convenience, remains the primary tool for clinical screening.However, traditional manual interpretation is time-consuming, experience-dependent, and prone to oversight and misdiagnosis.With the advancement of deep learning in medical imaging, computer visionbased automated analysis offers promising solutions to these challenges.Nevertheless, existing methods such as U-Net and its variants often struggle with accurately segmenting complex lung structures, especially when dealing with noisy images, small lesions, or blurred boundaries.Additionally, conventional 2D convolutional neural networks (CNNs) have limitations in capturing the spatial features inherent in chest X-ray images, and current multi-disease classification models still face challenges in achieving high accuracy and generalizability.To address these issues, this study proposes two key innovations: First, an optimized U-Net++L3 network with pruning is developed for automatic chest X-ray segmentation, effectively reducing parameter redundancy while maintaining accuracy, thereby enhancing segmentation performance in regions with complex lesions.Second, a densely connected 3D CNN model is designed for the recognition of multiple respiratory diseases.By leveraging the spatial feature extraction capabilities of 3D convolutions and the feature reuse advantages of dense connections, the model achieves precise classification of conditions such as pneumonia, lung cancer, and chronic obstructive pulmonary disease (COPD).The outcomes of this research aim to overcome the limitations of traditional models in terms of segmentation accuracy, computational efficiency, and feature representation, providing both theoretical innovation and practical value for rapid clinical screening and the enhancement of primary healthcare resources.