Advanced Federated Learning Techniques for Multi-Institutional Lung Disease Detection utilizing Chest Radiographs and Computed Tomography Scans

Detecting lung cancer at an early stage can reduce mortality, which is one of the most dangerous diseases. However, manual interpretation by health professionals can produce variable results. In this paper, an Advanced Federated Learning Techniques for Multi-Institutional Lung Disease Detection using Chest Radiographs and CT Scans (FL-MILDD-CR-CTS) is proposed. The input images are collected from chest X-ray and CT scan datasets and preprocessed using an Adaptive Tracking Dual Nested Kalman Filter (ATDNKF) for noise removal and enhancement. Dimensionality reduction is performed using Principal Component Analysis (PCA) to maintain essential information while reducing complexity. Feature extraction and disease classification are conducted using SVM, CNN and Random Forest, optimized for identifying complex lung patterns. The framework aggregates locally trained models using Federated Learning (FedAvg and FedProx) to preserve data privacy. The experimental results show that FL-MILDD-CR-CTS achieves 26.68% better accuracy and 30.94% better precision when compared to the existing models.