## 1 Introduction  
Prostate cancer [1] is currently one of the deadliest cancers in men, with a very high incidence and death rate each year. According to the World Health Organization, in 2020, about 1.41 million people suffered from prostate cancer and 380,000 died from it [2]. Early diagnosis and treatment of prostate cancer can be highly effective in preventing the development of cancerous tissue and metastasis into advanced prostate cancer, effectively improving the five-year survival rate of prostate cancer patients and reducing patients' suffering. The diagnosis of PCa is currently made clinically with a prostate-specific antigen (PSA) [3] blood test and digital rectal examination (DRE) [2], followed by a transrectal ultrasound (TRUS) biopsy if the PSA test result is positive. However, due to the limited number of biopsy samples and/or the low ultrasound resolution of TRUS [4], lesions may be missed or the Gleason score (GS) determined from the biopsy sample may differ in repeat biopsies and, sometimes, from the score determined by radical prostatectomy. Moreover, prostate cancer is classified as clinically severe or clinically non-severe based on the GS, which is currently \(\leq\)7 for clinically non-severe prostate cancer and \(\geq\)8 for clinically severe prostate cancer. According to recent studies [3; 5], the diagnosis of prostate cancer using PSA and biopsy has low sensitivity and specificity, which can lead to underdiagnoses and overtreatment, thus causing unnecessary suffering to patients. According to a recent study [6], the positive predictive values of DRE, TRUS, mpMRI, and TPSA levels for PCa were 39.91%, 39.38%, 64.14%, and 41.57%, respectively; the sensitivity of these parameters was 37.35%, 51.41%, 74.69%, and 57.43%, respectively; the specificity of these parameters was 62.26%, 46.90%, 71.97%, and 45.82%, respectively. Recent studies have demonstrated that multi-parametric magnetic resonance imaging (mp-MRI) [7; 8; 9] can provide a simpler, non-invasive, and more accurate method of detecting prostate cancer. By combining images different MRI modalities, these previous studies showed that mp-MRI images have a higher detection rate and better sensitivity and specificity for prostate cancer; because of the non-invasive and highly detectable nature of MRI, more and more studies are focusing on the classification of the clinical severity of prostate cancer under multiple modalities [10]. However, it is very difficult to manually perform operations such as classification and judgment of mp-MRI because there is a large number of images for each patient, thus requiring much time and expertise on the part of the radiologist for judging and interpretation analysis. In addition, due to the subjectiveness of the radiologist, there will be low sensitivity and specificity in analyzing and judging the images [11], especially in the articulation of different regions of the prostate. Therefore, there is a need for a computer-assisted prostate cancer classification method that can reduce the time required to classify prostate cancer and improve the specificity and sensitivity of prostate cancer diagnosis. In recent studies [6; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21], methods were developed for automatic prostate cancer detection, diagnosis, and classification. Currently, the prostate cancer diagnosis method consists of three main parts: first, data pre-processing (cropping the overall prostate image to the prostate region or specific cancer site region); second, inputting the pre-processed image into a deep learning network for feature learning to obtain a feature map of the prostate; finally, outputting the results of the cancer grade according to the voxels in the learned feature map. The first computer-aided diagnosis system for prostate cancer, which was designed by [PERSON] et al. [22], extracts pixel features from T2-weighted images (T2) with a matrix and discrete cosine transform, and then uses an SVM classifier to classify the peripheral regions of the prostate. In addition, [PERSON] et al.