Multiparametric MRI (mp-MRI) can localize tumour within the prostate, guide biopsy, and assess disease burden. Nevertheless, mp-MRI itself remains imperfect. Almost 40% of mp-MRI studies are reported as indeterminate for significant cancer. An indeterminate mp-MRI confers no patient benefit, such patients require either repeat interval mp-MRI and/or subsequent biopsy. There remains a clear unmet need to improve diagnostic imaging over and above standard mp-MRI protocols. Previous work has developed zone specific logistic regression (LR) models for the determination of significant cancer (any cancer-core-length (CCL) with Gleason>3+3 or any grade with CCL≥4 mm) in the peripheral (PZ) and the transition zone (TZ) based on quantitative mp-MRI parameters following MR imaging. This work proposes a state-of-art deep learning method to detect prostate cancer both in the PZ and the TZ. The proposed model is trained on a cohort of patients imaged at a 3T scanner, and validated on independent cohort of patients imaged at a 1.5T scanner. The performance of the model was compared with LR, Support Vector Machines and traditional Neural Networks.