Prostate Cancer Detection in Urine Using the Fusion of LIBS, FTIR Dual Spectra and FTIR Reconstructed Image

Prostate cancer (PCa) is one of the most common malignant tumors in men, necessitating the use of effective methods for early detection. This study proposes a multimodal approach based on urine analysis using laser-induced breakdown spectroscopy (LIBS) and Fourier transform infrared spectroscopy (FTIR). Specifically, we developed a dual-spectrum reconstructed image fusion network (SMFNet) incorporating a spectrum-to-image reconstruction strategy and optimized loss functions (feature margin and focus loss). By leveraging the complementarity between atomic and molecular spectral data, the SMFNet model enhances feature characterization and interclass differentiation. The results demonstrate that SMFNet achieves an accuracy of 97.62% and a macro-F1 of 98.85% on the test set, significantly outperforming single-modal methods and baseline models. Consequently, this method offers a novel, efficient, and reliable approach for the future early detection of PCa, with the potential to enhance diagnostic accuracy, shorten detection cycles, and provide a supportive basis for early clinical intervention.