Affiliations: [a] Department of Electrical Engineering, Mahshahr Branch, Islamic Azad University, Mahshahr, Iran
| [b] Department of Electrical Engineering, Faculty of Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran
Correspondence:
[*]
Address for correspondence: Gholamreza Akbarizadeh, Department of Electrical Engineering, Faculty of Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran. E-mail: [email protected].
Abstract: Since prostate cancer is one of the most important causes of death in today’s society, the investigation of why and how to diagnose and predict it has received much attention from researchers. The cooperation of computer and medical experts provides a new solution in analyzing these data and obtaining useful and practical models, which is deep learning. In fact, deep learning as one of the most important tools for analyzing data and discovering relationships between them and predicting the occurrence of events is one of the practical tools of researchers in this way. This study segments and classifies prostate cancer using a deep learning approach and architectures tested in the ImageNet dataset and based on a method to identify factors affecting this disease. In the proposed method, after increasing the number of data based on removing dominant noises in MRI images, image segmentation using a network based on deep learning called faster R-CNN, and then feature extraction and classification with architecture Various deep learning networks have reached the appropriate accuracy and speed in detection and classification. The aim of this study is to reduce unnecessary biopsies and to choose and plan treatment to help the doctor and the patient. Achieving the minimum error in the diagnosis of malignant lesion with a criterion called Sensitivity of 93.54% and AUC equal to 95% with the ResNet50 architecture has achieved the goal of this research.
Keywords: Magnetic Resonance Imaging (MRI), prostate cancer prediction, morphological properties, deep learning architecture, transfer learning, Receiver Operating Characteristic (ROC) Curve
