## 1 Introduction
[23] classified the peripheral zone (PZ) of the prostate using a dynamic contrast-enhanced (DCE) map, and [PERSON] et al. [24] designed a classification system using semi-supervised multi-modal data. However, these studies separated different regions of the prostate, causing cancer at the junction of different regions to be easily missed and global features of the prostate to be ignored. Many recent studies focused on improving neural network models, but it is known that deep learning is still almost a black-box [25] system, and the intermediate learning process is difficult to understand. Therefore, there is the field of explainable deep learning, including CAM (class activation mapping) [26] technology, which uses feature visualization to explore the working mechanisms of deep convolutional neural networks and the basis of judgment. However, when implementing CAM, it is necessary to change the structure of the network itself; thus, Grad-cam was investigated on the basis of CAM [27]. Grad-cam can be implemented without changing the structure of the network itself and can extract the heat map of features of any layer, and a recent study investigated Grad-cam++ [28] in order to optimize the results of Grad-cam and make the positioning more accurate. In this paper, we design a novel method for prostate cancer classification based on fusing image features under multiple modalities to enable the classification of the clinical severity of prostate cancer with a single input rather than using a costly multiple-input method with complex training. Specifically, we align the T2 and DWI images of the same patient to align the prostate region in space, crop the whole MRI image to the prostate region, fuse the aligned images with the T2 and DWI images to form a new 3D image sequence (\"sequence\" is used in this article to refer to the \"input sequence\" of a neural network, not to refer to an MRI acquisition sequence), and then input the new 3D sequence into the 3D-CNN for feature learning. Finally, we output the features for prostate cancer severity classification and visualize the learning interest points of the network using the improved 3D-Grad-cam. In this study, there are three main contributions:  
1. We developed a novel 3D-CNN input method that maintains the advantage of a low training cost for a single input and the advantage of multi-modal feature fusion of previous multi-input models, such that the model can fully fuse multi-modal features and facilitate network prediction with a single input. 2. We improve the category activation map based on CAM by using the category activation map in a 3D image sequence to obtain a 3D-Grad-cam to facilitate our visualization of the network learning process. 3. We performed an extensive experimental evaluation and comparison and used different 3D-CNN models and different sampling methods for 3D-CNN models, and the AUC, sensitivity, and specificity of this method on a test dataset were 0.85, 0.88, and 0.88, respectively. The rest of the paper is structured as follows. The following section focuses on the proposed method and the dataset used for the experiments, the Section 3 presents the experimental results and compares the baseline with the latest methods, the discussion is presented in the Section 4, and, finally, the conclusions are presented.