Scalable Distributed Deep Learning for Lung Disease Diagnosis: A Spark–Elephas Cross-Modality Framework with GPU Cluster Scalability Analysis

Résumé fourni par la source

The rapid growth of large-scale medical imaging datasets has exposed critical limitations of single-node deep learning workflows for clinical decision support in lung disease diagnosis. We present a unified, Spark-based distributed framework for scalable multi-class lung disease classification from both chest X-ray (CXR) and computed tomography (CT) images. Our approach integrates Apache Spark with Elephas to enable synchronous and asynchronous data-parallel training of Keras models via a parameter-server architecture on a GPU-enabled cluster, supporting binary and multi-class classification across multiple pulmonary pathologies including COVID-19 pneumonia, viral pneumonia, lung opacity, and normal cases. We implement transfer learning with DenseNet169, ResNet50, and MobileNetV2 and evaluate three classification tasks: binary lung disease detection and three-class diagnosis on CXR images, and three-class diagnosis on CT scans. Experiments on large public datasets demonstrate that the proposed system achieves high diagnostic performance, reaching 97.63% accuracy for three-class CXR and 95.49% for binary CT classification, while providing substantial runtime gains over single-node baselines. On a multi-node Spark cluster, synchronous training attains near-linear to super-linear scaling with speedups up to 6.26× and parallel efficiency exceeding 156%, attributed to improved cache utilization and reduced I/O contention across distributed workers. Comparative analysis consistently demonstrates that synchronous parameter updates outperform asynchronous training in both convergence stability and final diagnostic accuracy across all tasks and modalities. This work delivers an end-to-end, cross-modality, distributed deep learning pipeline with explicit scalability characterization. The proposed framework is generalizable beyond pulmonary imaging and demonstrates how cluster computing infrastructures can be leveraged to build scalable, high-throughput experimental pipelines for multi-class lung disease classification in research settings.

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