  Prostate cancer is the most dangerous cancer diagnosed in men worldwide.
Prostate diagnosis has been affected by many factors, such as lesion
complexity, observer visibility, and variability. Many techniques based on
Magnetic Resonance Imaging (MRI) have been used for prostate cancer
identification and classification in the last few decades. Developing these
techniques is crucial and has a great medical effect because they improve the
treatment benefits and the chance of patients' survival. A new technique that
depends on MRI has been proposed to improve the diagnosis. This technique
consists of two stages. First, the MRI images have been preprocessed to make
the medical image more suitable for the detection step. Second, prostate cancer
identification has been performed based on a pre-trained deep learning model,
InceptionResNetV2, that has many advantages and achieves effective results. In
this paper, the InceptionResNetV2 deep learning model used for this purpose has
average accuracy equals to 89.20%, and the area under the curve (AUC) equals to
93.6%. The experimental results of this proposed new deep learning technique
represent promising and effective results compared to other previous
techniques.
