  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.
