Prostate cancer (PCa) is a severe disease among men globally. It is important to identify PCa early and make a precise diagnosis for effective treatment. For PCa diagnosis, Multi-parametric magnetic resonance imaging (mpMRI) emerged as an invaluable imaging modality that offers a precise anatomical view of the prostate gland and its tissue structure. Deep learning (DL) models can enhance existing clinical systems and improve patient care by locating regions of interest for physicians. Recently, DL techniques have been employed to develop a pipeline for segmenting and classifying different cancer types. These studies show that DL can be used to increase diagnostic precision and give objective results without variability. This work uses well-known DL models for the classification and segmentation of mpMRI images to detect PCa. Our implementation involves four pipelines; Semantic DeepSegNet with ResNet50, DeepSegNet with recurrent neural network (RNN), U-Net with RNN, and U-Net with a long short-term memory (LSTM). Each segmentation model is paired with a different classifier to evaluate the performance using different metrics. The results of our experiments show that the pipeline that uses the combination of U-Net and the LSTM model outperforms all other combinations, excelling in both segmentation and classification tasks.

翻译：前列腺癌（PCa）是全球男性面临的严重疾病。早期识别并精确诊断PCa对于有效治疗至关重要。多参数磁共振成像（mpMRI）作为一种无创的成像方式，能够提供前列腺腺体及其组织结构的精确解剖视图，已成为PCa诊断的重要工具。深度学习（DL）模型可通过定位医师感兴趣的区域，增强现有临床系统并改善患者护理。近年来，DL技术被用于开发不同癌症类型的分割与分类流水线。研究表明，DL可提升诊断精确性并提供无变异性的客观结果。本研究采用主流DL模型对mpMRI图像进行分类与分割以检测PCa。我们实现了四条流水线：语义DeepSegNet与ResNet50、DeepSegNet与循环神经网络（RNN）、U-Net与RNN、以及U-Net与长短期记忆网络（LSTM）。每个分割模型与不同分类器配对，采用多种指标评估性能。实验结果表明，采用U-Net与LSTM组合的流水线在分割与分类任务中均优于其他所有组合。