Hybrid CNN-Mamba Architecture for Automated Detection of Pulmonary Abnormalities in Chest X-Ray Images
Keywords:
pulmonary abnormality detection; chest X-ray; hybrid deep learning; state space models; Mamba; medical imaging systemsAbstract
Chest X-ray imaging remains one of the most widely used diagnostic modalities for pulmonary disease, yet the rapid and reliable interpretation of these images continues to strain radiology services worldwide. Automated detection systems based on convolutional neural networks have demonstrated strong local feature extraction, but they often struggle to model long-range anatomical and pathological dependencies. Transformer-based architectures address some of these limitations but introduce significant computational and memory burdens. This paper presents a hybrid CNN-Mamba architecture for automated detection of pulmonary abnormalities in chest X-ray images. The proposed system combines local convolutional feature learning with selective state space sequence modeling to capture both fine-grained textural detail and distributed contextual relationships across the lung fields. Rather than focusing exclusively on classification accuracy, this paper provides a system-level analysis of the architectural trade-offs, data governance requirements, training strategies, robustness, fairness, interpretability, deployment constraints, sustainability, and policy implications associated with the hybrid design. The discussion emphasizes that a clinically viable pulmonary abnormality detection system must be evaluated not only as a predictive model but also as a socio-technical infrastructure embedded within existing radiology workflows, regulatory frameworks, and institutional governance structures. The hybrid CNN-Mamba approach offers a promising balance between representational capacity and operational efficiency, but its translation into practice depends on rigorous external validation, transparent uncertainty communication, equitable performance across populations, and alignment with evolving medical device oversight mechanisms.
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