## 3 Experiments  
### Setup  
We collected T2 and DWI images of the prostate, which were used to train the model and evaluate the performance of the model. The prostate MRI data used in this paper consisted of 129 samples from Toho University Medical Center, Japan (dataset A) and were acquired with 3T MR scanners (SIEMENS Skyra; syngo MR E11), as well as 121 samples from the 2017 SPIE-AAPM-NCI PROSTATEx challenge dataset (dataset B). The PROSTATEx Challenge [40] (\"SPIE-AAPM-NCI Prostate MR Classification Challenge\") was held in conjunction with the 2017 SPIE Medical Imaging Symposium and focused on quantitative image analysis methods for diagnostic purposes and clinically meaningful prostate cancer classification. For each patient, the image with the most significant lesion area was selected. The two datasets used--with data from different sites--were collected using different devices. We performed regularization preprocessing on these two datasets, as in the previous step. The method proposed in this paper was mainly used to predict high and low risk of early prostate cancer (according to the Gleason score, a score greater than or equal to 8 is considered clinically severe, and a score less than or equal to 7 is considered clinically insignificant). We used three main evaluation criteria to assess the performance of the model: the AUC (area under curve) value, sensitivity, and specificity, with the AUCbeing defined as the area under the ROC curve. Sensitivity (Se), called the true-positive fraction (TPF; or true-positive rate (TPR)), is the probability that a diagnostic test is correctly diagnosed as positive in a case group. Specificity (Sp), called the true-negative rate (TNF; or true-negative rate (TNR)), is the probability that the diagnostic test is correctly diagnosed as negative in the control group, and the false-negative rate (FNR; or false-negative fraction, FNF) is the probability that the diagnostic test is negative in the case group, which will lead to delayed disease and treatment. The false-positive fraction (FPF; or false positive rate (FPR)) is the probability that a diagnostic test is incorrectly diagnosed as positive in the control group. A false positive will result in incorrect treatment, and patients sometimes suffer from risky confirmatory tests. \[\text{sensitivity}=\frac{\text{TP}}{\text{TP}+\text{FN}} \tag{2}\]  
\[\text{specificity}=\frac{\text{TN}}{\text{FP}+\text{TN}} \tag{3}\]  
In the above equation, TP, TN, FP, and FN represent the true positive, true negative, false positive, and false negative, respectively. In the following sections, experiments are conducted to evaluate the performance of the proposed method in this paper. Table 1 shows the comparison experiments for the different 3D-CNN models, Table 2 shows the comparison experiments for the different newly ordered input sequences, and Table 3 shows the comparison experiments for the different modalities of the original image sequence; a comparison with the 3 latest methods is shown in Table 4. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Methods** & **Sensitivity** & **Specificity** & **AUC** & **CI 95\%** & **Parameters** \\ \hline C3D & 0.83 & 0.79 & 0.81 & 0.80–0.83 & 78 M \\
3 DSqueezeNet & 0.73 & 0.68 & 0.70 & 0.72–0.78 & 2.15 M \\
3 DMobileNet & 0.74 & 0.67 & 0.69 & 0.73–0.75 & 8.22 M \\
3 DShuffleNet & 0.74 & 0.65 & 0.68 & 0.74–0.76 & 6.64 M \\ ResNext101 & 0.83 & 0.75 & 0.81 & 0.76–0.82 & 48.34 M \\
3 DResnet101 & 0.88 & 0.88 & 0.83 & 0.84–0.85 & 83.29 M \\ Our method + 3 DResNet50 & 0.88 & 0.84 & 0.85 & 0.85–0.87 & 44.24 M \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison with 3D-CNN methodologies. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Methods** & **Sensitivity** & **Specificity** & **AUC** & **CI 95\%** & **Parameters** \\ \hline Order 1 & 0.88 & 0.88 & 0.85 & 0.85–0.87 & 44.24 M \\ Order 2 & 0.84 & 0.84 & 0.82 & 0.80–0.83 & - \\ Order 3 & 0.84 & 0.84 & 0.81 & 0.82–0.84 & - \\ Order 4 & 0.88 & 0.84 & 0.84 & 0.79–0.84 & - \\ \hline \hline \end{tabular}
\end{table}