Benign-Malignant Classification of Pulmonary Nodules in CT Images Based on Fractal Spectrum Analysis

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Abstract

This study reveals that pulmonary nodules exhibit distinct multifractal characteristics, with malignant nodules demonstrating significantly higher fractal dimensions at larger scales. Based on this fundamental finding, an automatic benign-malignant classification method for pulmonary nodules in CT images was developed using fractal spectrum analysis. By computing continuous three-dimensional fractal dimensions on 121 nodule samples from the LIDC-IDRI database, a 201-dimensional fractal feature spectrum was extracted, and a simplified multilayer perceptron neural network (with only 6x6 minimal neural network nodes in the intermediate layers) was constructed for pulmonary nodule classification. Experimental results demonstrate that this method achieved 96.69% accuracy in distinguishing benign from malignant pulmonary nodules. The discovery of scale-dependent multifractal properties enables fractal spectrum analysis to effectively capture the complexity differences in multi-scale structures of malignant nodules, providing an efficient and interpretable AI-aided diagnostic method for early lung cancer diagnosis.

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