Multi-Textural Deep Feature Fusion for Four-Class Lung Lesion Classification from Computed Tomography and Positron Emission Tomography Images

Distinguishing malignant pulmonary lesions from benign nodules, infection-related abnormalities, and normal findings remains difficult because structural and metabolic imaging patterns overlap. This study presents a multi-textural feature-fusion framework for four-class classification using independent computed tomography (CT) and positron emission tomography (PET) cohorts comprising 1213 CT and 1013 PET images. Most patients contributed one representative image and some contributed two; known same-patient images were kept together when linkage was available, but complete linkage was not retained after de-identification, so the protocol is described as hybrid and partially patient-aware. Original images were complemented by DCT-based illumination-normalized representations and one-level stationary-wavelet representations, and the two transformed branches were combined through DWT-based multi-textural fusion before NNMF, multi-backbone integration, mRMR selection, and multiclass SVM classification. The best PET model achieved 0.930 accuracy, 0.885 macro F1-score, and 0.868 MCC; the corresponding CT values were 0.878, 0.781 macro F1-score, and 0.759 MCC. Benign findings remained the least stable category. Higher PET performance reflects these independent institutional cohorts and display-image inputs and should not be interpreted as intrinsic superiority of PET over CT. The study remains limited to representative two-dimensional images and internal validation.