## 1 Introduction
In addition to imaging findings, lymph node dissection remains the most reliable method for lymph node staging. For metastasis (M) staging, multimodal imaging techniques such as CT, MRI, positron emission tomography (PET), and 99 mTc-bone scans are used. Presently, the interpretation of multiparametric MRI data is entirely performed by radiologists. Although they are competent, it is difficult for them to deal with increasing imaging demands within a limited timeframe. Also, there is significant variability between observers since performance depends on their experience [11; 12]. In a report by [PERSON] et al., the inter-observer variability of prostate MRI using PI-RADS was investigated outside high-volume centers [13]. Several studies have been conducted on the automation of the whole or a part of the diagnosis workflow to improve diagnostic accuracy and reduce the costs and workload of healthcare personnel. In recent years, computer-aided diagnosis (CAD) has been actively investigated based on advances in deep learning technologies, such as convolutional neural networks (CNN), which have rapidly developed in the medical imaging field with advances in artificial intelligence (AI). Because CNNs are adept at discovering complex structures in high-dimensional data, they are powerful tools for image classification and segmentation [14; 15; 16]. For example, [PERSON] et al. proposed a deep learning-based approach in order to automatically diagnose temporomandibular disorder on magnetic resonance (MR) images with seven different fine-tuned, pre-trained CNNs: Xception, ResNet-101, MobileNetV2, InceptionV3, DenseNet-121, ConvNeXt, and Vision Transformer (ViT) [17]. However, CNN image classification assesses the entire image and can rely on features other than the lesion area. Consequently, even if the accuracy of the classifier is high, its reliability is reduced if the judgment is based on areas other than the lesion. A three-dimensional CNN (3D-CNN), which extends the two -dimensional CNN (2D-CNN), which is widely used in image recognition, is used for image classification and motion recognition of 3D data. Unlike 2D-CNN, 3D-CNN can extract 3D features [18]. Semantic segmentation is a deep learning algorithm that enables pixel-level image classification and can detect irregularly shaped objects. A comprehensive assessment of multiparametric MRIs consists of eight or more different volumetric image datasets. This is a burden for the radiologists. Even for experienced radiologists, it is difficult to detect subtle cancerous lesions expressed within multiparametric MRIs. Moreover, it is important to correlate characterized cancerous lesions in multiparametric MRIs and biopsy findings with GS for non-invasive GS prediction. Based on the various quantitative image features, various deep-learning systems have been investigated for prostate cancer detection and diagnosis using multiparametric MRI with detection and classification tasks. There were only a small number of CAD systems for prostate cancer. In related work, [PERSON] et al. developed a DWI-based CAD system that utilized three intensity features and a K-nearest neighbor classifier to distinguish between benign and malignant cases [19]. [PERSON] et al. developed the multiparametric MRI-based CAD system to detect prostate cancer in the peripheral zone using T2 WI, dynamic contrast-enhanced MRI and DWI [20]. [PERSON] et al. developed the prostate-segmentation technique using a combination of features such as the apparent diffusion coefficient map (ADCmap). [PERSON] et al. proposed the use of multichannel image-to-image convolutional encoder-decoders to directly determine tumor malignancy without performing an invasive prostate biopsy procedure [21]. [PERSON] et al. proposed an approach focused on the automatic GS classification, which exploited a set of 18 radiomic features directly obtainable from segmented MRI [22].