PneumoMamba : A Novel Mamba and CNN Dual‐Path Network for Computer‐Aided Diagnosis of Pneumonia Using Omnidirectional Feature Extraction and Multi‐Scale Asymmetric Convolution

Pneumonia classification from chest x‐ray images remains a challenging task because different pneumonia categories often exhibit highly similar visual manifestations, while subtle lesion regions require both fine‐grained local detail perception and effective global contextual modeling. To address these challenges, we propose PneumoMamba, a dual‐path network for computer‐aided pneumonia diagnosis that combines a convolutional neural network branch for local texture extraction with a Mamba‐based branch for efficient long‐range dependency modeling. In the proposed framework, the convolutional branch focuses on capturing detailed local structures, whereas the Mamba‐based branch is designed to model global contextual information with linear computational complexity. To better preserve spatial continuity for sequence modeling, we further introduce an eight‐directional scan strategy (8DScan), which converts two‐dimensional feature maps into multidirectional scan sequences for more comprehensive spatial dependency modeling. In addition, a Multi‐Scale Asymmetric Convolution (MSAConv) and a focused feature module (FFM) are incorporated to enhance the representation of subtle pneumonia‐related patterns. We evaluate the proposed method on the Pneumonia‐CXR‐Database, which contains four classes of chest x‐ray images, under a 6:2:2 train/validation/test split. Experimental results show that PneumoMamba achieves a test accuracy of 94.94%, together with strong F1‐score, sensitivity, and precision, outperforming representative comparison methods. These results demonstrate the effectiveness of the proposed framework for computer‐aided pneumonia classification.