FFT-Mamba-guided Lung Parenchyma Segmentation and Radiomics Representation for COPD Staging Diagnosis

Chronic obstructive pulmonary disease (COPD) is a common chronic respiratory disease with a high mortality rate. Early diagnosis of risk grading is of great significance for clinical guidance and treatment. In this paper, we propose an automated COPD staging diagnosis model by combining deep/machine learning and computed tomography (CT) scan analysis. This model is composed of three parts: lung parenchyma segmentation, imaging feature analysis, and classification. Firstly, the fast Fourier transform (FFT)-guided dual branch Mamba model is proposed achieve accurate lung parenchyma segmentation in 2-dimensional CT slice images, in which FFT can improve the model's performance to capture edge and texture information. Next, the Radiomics is employed to analysis the features of segmented 3-dimensional lung parenchyma in CT scans. After effective feature selection, the machine learning classifiers (i.e., K-Nearest Neighbor, Linear Discriminant Analysis, and Support Vector Machine) are used to achieve staged diagnosis of patients with COPD. The experimental results demonstrate that the lung parenchyma segmentation model proposed in this paper has good segmentation performance in CT slice images, with IoU of 0.9819 and Dice of 0.9908. Meanwhile, through 5-fold cross validation, the support vector machine classifier can obtain the best classification performance for COPD staging diagnosis, with the accuracy of 88.57%. The above results prove that the model proposed in this paper has superior diagnosis performance and clinical application potential in small sample situations.