Emphysema is a hallmark of Chronic Obstructive Pulmonary Disease and an independent risk factor for lung cancer. Computed Tomography (CT) is the main diagnostic platform for identifying emphysema. In clinical practice, the quantitative assessment identifies emphysema as low attenuation areas under a specific cut-off threshold set to -950 Hounsfield Unit. Despite its wide adoption, this method lacks consensus on an optimal cut-off threshold and is prone to measurement variation, asking for new solutions that encompass this limitation. We propose a hybrid deep learning approach for emphysema classification that combines convolutional neural networks for local feature extraction with MAMBA’s capability to model long-range dependencies. This fusion ensures a complementary feature representation, capturing both fine-grained and global contextual information. Furthermore, we demonstrate the effectiveness of self-supervised pretraining in domain-specific data, refining the weight configuration of the model to better align with the target distribution and improve its performance during supervised training. The results show on the SCAPIS public cohort that our hybrid model not only outperforms the traditional LAV950 method for emphysema quantification but also surpasses two well-established deep learning architectures.