> The dataset was randomly divided into 70% training subset (1420 for prostate and 692 for pancreatic), 15% validation subset (304 and 148), and 15% test subset

> The dataset was randomly divided into 70% training subset (1420 for prostate and 692 for pancreatic), 15% validation subset (304 and 148), and 15% test subset (304 and 148). The batch training task was performed based on conjugate gradient model for 100 epochs with a learning rate of 1e−5. The RNN deep learning model trained for discrimination of normal and prostate cancer urines showed the best validation performance at 93 epochs (Figs. S12a–c). As shown in Fig. S12b, using the total of 304 human urine test datasets, our RNN binary classification model achieved a 0.9997 AUC from the receiver operating characteristic (ROC) curve and a sensing performance with a sensitivity of 99.4%, a specificity of 100%, and an accuracy of 99.7% (confusion matrix). In the same manner, the trained deep learning for pancreatic cancer urine dataset (Figs. S13a–c) clearly showed superior classification performance with a sensitivity of 98.6% and a specificity of 100% (99.3% accuracy, 0.9892 AUC, 59 epochs). The false discovery rate (FDR) of RNN-assisted binary classification was 0%, 0.7%, and 1.3% for normal, prostate cancer, and pancreatic cancer, respectively. In CNN-assisted three-class classification, the dataset was randomly divided into 70% training subset (1,557) and 30% test subset (666). The batch training task was performed based on conjugate gradient model for 100 epochs with single conventional layer. To find more general diagnostic model for unknown urine sample analysis, multi-class classification method was applied on normal, prostate and pancreatic cancer urine based on CNN deep leaning model. The CNN deep learning model trained for normal and cancer type classification showed excellent performance with <1% loss and 100% accuracy (Fig. S14) after 33 epochs. Our CNN multi-class classification model achieved a sensing performance with a sensitivity of 96.8%, a specificity of 99.6% (prostate) and 97.7% (pancreatic), and an accuracy of 96.8% (confusion matrix) from test dataset. The FDR of CNN-assisted multi-class classification was 4.4%, 1.8%, and 3.6% for normal, prostate cancer, and pancreatic cancer, respectively. Therefore, our 3D-PCNUS platform supported by a fast deep learning bioclassification method successfully distinguishes cancer urine from normal urine with high clinical sensitivity and specificity, suggesting the use of the developed system for on-site cancer diagnostic technology. The developed system is suitable for patient screening with higher pathogenic probability.