This open-access journal is published under the terms of the Creative Commons Attribution-Noncommercial 4.0 International License.

Prostate cancer is one of the most common cancers in men, which takes many victims every year due to its latent symptoms. Thus, early diagnosis of the extent of the lesion can help the physician and the patient in the treatment process. Nowadays, detection and labeling of objects in medical images has become especially important. In this article, the prostate gland is first detected in T2 W MRI images by the Faster R-CNN network based on the AlexNet architecture and separated from the rest of the image. Using the Faster R-CNN network in the separation phase, the accuracy will increase as this network is a model of CNN-based target detection networks and is functionally coordinated with the subsequent CNN network. Meanwhile, the problem of insufficient data with the data augmentation method was corrected in the preprocessing stage, for which different filters were used. Use of different filters to increase the data instead of the usual augmentation methods would eliminate the preprocessing stage. Also, with the presence of raw images in the next steps, it was proven that there was no need for a preprocessing step and the main images could also be the input data. By eliminating the preprocessing step, the response speed increased. Then, in order to classify benign and malignant cancer images, two deep learning architectures were used under the supervision of ResNet18 and GoogleNet. Then, by calculating the Confusion Matrix parameters and drawing the ROC diagram, the capability of this process was measured. By obtaining Accuracy = 95.7%, DSC = 96.77% and AUC = 99.17%, The results revealed that this method could outperform other well-known methods in this field (DSC = 95%) and (AUC = 91%).

Since prostate cancer is one of the most important causes of mortality in today's society, the study of why and how to diagnose and predict them has received much attention from researchers. The collaboration of computer and medical experts offers a new solution in analyzing this data and obtaining useful and practical models, which is data mining. In fact, data mining, as one of the most important tools for data analysis and discovering the relationships between them and predicting the occurrence of events is one of the practical tools of researchers in this way. This study diagnoses and classifies prostate cancer using Deep Learning approach and MobileNetV2 architecture based on a method to identify the factors affecting this disease. In this study, data was taken from a database on the Brigham Hospital website. Also, in order to improve the methods of diagnosing prostate cancer, a feature-classification approach has been proposed, which has been evaluated using a data set related to clients' files. The proposed method after applying various classification methods on the available data including benign and malignant diagnosis and reaching an optimal method with relatively high accuracy using a faster R-CNN network to segment the area and later using architecture Various convolutional neural networks (CNNs) have been selected for feature extraction and set classification, increased processing speed. In addition, the MobileNetV2 architecture is used, which has the ability to achieve AUC in the range of 0.87 to 0.95 with acceptable performance, high processing speed and relative accuracy for the diagnosis of prostate cancer.

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.