The development of medical big data analysis technology has enabled vast imaging datasets to play an important role in identifying complex pathologies, supporting individualized diagnoses, and enhancing diagnostic and treatment precision. Pediatric pneumonia, a leading cause of mortality in children, results from infections and is characterized by inflammation of the terminal airways, alveoli, and pulmonary interstitium. Accurate and timely diagnosis is essential for successful intervention. However, most current studies are constrained by simplistic classification tasks, and existing models struggle to effectively extract multi-scale features from Chest X-Ray (CXR) images while eliminating redundant information. Moreover, the datasets used in existing studies lack the diversity and scale necessary to meet the demands of clinical practice. To address these challenges, this paper presents the a multi-branch dual residual attention Convolutional Neural Network (CNN) model for pediatric pneumonia classification model (namely PP-ConvNeXt), designed to enable automatic diagnosis of pediatric pneumonia. First, the model incorporates a novel multi-branch dual-residual structure, enhancing its capacity to extract multi-scale features. Second, the Coordinate Attentional Feature Fusion (CAFF) module is introduced to integrate cross-branch features, allowing for comprehensive acquisition of lesion information. Finally, this research pioneers the automatic five-class diagnosis of pediatric pneumonia, offering clinicians more accurate diagnostic insights. Additionally, a comprehensive dataset comprising 5632 CXR images from Hainan Women and Children’s Medical Center (HWCMC) is presented. Experimental results indicate that the PP-ConvNeXt model surpasses state-of-the-art models and expert human diagnosticians in the five-class diagnosis of pediatric pneumonia, achieving an Area Under the Curve (AUC) score of 89.01%. Additionally, key performance metrics, including accuracy and recall, further validate the model’s superior performance.

Cyber-Physical-Social Intelligence (CPSI) enhances the interpretability of Gradient Descent (GD) optimizers and improves image recognition models. To address the black-box nature of optimizers, we propose a new approach to map the GD optimizer to the frequency domain, allowing analysis of gradient variations at different frequencies. This approach aids in selecting optimal training and optimization strategies, offering a novel solution to the challenge of optimizer interpretability. Specifically, firstly, based on the transfer function of the gradient descent optimizer, the real-frequency characteristic and imaginary-frequency characteristic functions of the optimizer are derived for the first time, which provide a new perspective for the selection of the optimizer and the interpretability of the tuning parameter. Then, based on these characteristic functions, the learning properties of the optimizer in the frequency domain are analyzed for the first time, which provides an important reference for improving the performance of the optimizer in real application problems. Finally, the effectiveness of the frequency domain modulation properties is verified through convex and non-convex optimization problems. Experimental results show that the proposed theory not only improves the recognition accuracy, convergence speed, and stability, but also extends its scope in practical applications.