Abstract

Background: Chest X-ray imaging is a widely used diagnostic modality for identifying various lung diseases. Accurate multiclass classification of lung diseases enables timely treatment and improves patient survival. However, disease detection using chest X-ray images remains challenging due to heterogeneous data, overlapping radiographic features, and data privacy concerns. Furthermore, distinguishing among different lung diseases is difficult because of their similar clinical manifestations and imaging characteristics. Method: To address these challenges, a novel chaotic evolution optimization-enabled deep channel-attention broad convolutional neural network (CCEO-DCABNet) is proposed for multiclass lung disease classification within a federated learning (FL) framework. The proposed model ensures enhanced data privacy by allowing multiple client nodes and a central server to collaboratively train the model without sharing raw data. Prior to classification, image preprocessing is performed using Gaussian filter-based denoising followed by multiscale unsharp masking-based image sharpening. Subsequently, multiclass disease classification is carried out using DCABNet, whose parameters are optimized through the proposed CCEO algorithm. In addition, the federated learning process employs an averaging strategy for local model updates and global aggregation. Results: The proposed CCEO-DCABNet achieves an accuracy, true positive rate (TPR), and true negative rate (TNR) of 96.98%, 96.41%, and 97.45%. Conclusions: Experimental results demonstrate that the proposed CCEO-DCABNet framework effectively classifies multiple lung diseases from chest X-ray images while preserving data privacy through federated learning. The model achieves superior classification performance and can support reliable computer-aided diagnosis in clinical settings.

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