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Prostate cancer is the most dangerous cancer diagnosed in men worldwide.Prostate diagnosis has been affected by many factors, such as lesioncomplexity, observer visibility, and variability. Many techniques based onMagnetic Resonance Imaging (MRI) have been used for prostate canceridentification and classification in the last few decades. Developing thesetechniques is crucial and has a great medical effect because they improve thetreatment benefits and the chance of patients' survival. A new technique thatdepends on MRI has been proposed to improve the diagnosis. This techniqueconsists of two stages. First, the MRI images have been preprocessed to makethe medical image more suitable for the detection step. Second, prostate canceridentification has been performed based on a pre-trained deep learning model,InceptionResNetV2, that has many advantages and achieves effective results. Inthis paper, the InceptionResNetV2 deep learning model used for this purpose hasaverage accuracy equals to 89.20%, and the area under the curve (AUC) equals to93.6%. The experimental results of this proposed new deep learning techniquerepresent promising and effective results compared to other previoustechniques.

An Enhanced Deep Learning Technique for Prostate Cancer Identification Based on MRI Scans