Implementation of Convolutional Neural Network Architecture on 3D Multiparametric Magnetic Resonance Imaging for Prostate Cancer Diagnosis

• URL: http://arxiv.org/abs/2112.14644v1
• Date: Wed, 29 Dec 2021 16:47:52 GMT
• Title: Implementation of Convolutional Neural Network Architecture on 3D Multiparametric Magnetic Resonance Imaging for Prostate Cancer Diagnosis
• Authors: Ping-Chang Lin, Teodora Szasz, and Hakizumwami B. Runesha
• Abstract summary: We propose a novel deep learning approach for automatic classification of prostate lesions in magnetic resonance images. Our framework achieved the classification performance with the area under a Receiver Operating Characteristic curve value of 0.87. Our proposed framework reflects the potential of assisting medical image interpretation in prostate cancer and reducing unnecessary biopsies.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Prostate cancer is one of the most common causes of cancer deaths in men. There is a growing demand for noninvasively and accurately diagnostic methods that facilitate the current standard prostate cancer risk assessment in clinical practice. Still, developing computer-aided classification tools in prostate cancer diagnostics from multiparametric magnetic resonance images continues to be a challenge. In this work, we propose a novel deep learning approach for automatic classification of prostate lesions in the corresponding magnetic resonance images by constructing a two-stage multimodal multi-stream convolutional neural network (CNN)-based architecture framework. Without implementing sophisticated image preprocessing steps or third-party software, our framework achieved the classification performance with the area under a Receiver Operating Characteristic (ROC) curve value of 0.87. The result outperformed most of the submitted methods and shared the highest value reported by the PROSTATEx Challenge organizer. Our proposed CNN-based framework reflects the potential of assisting medical image interpretation in prostate cancer and reducing unnecessary biopsies.

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