Transfer learning; powerful and fast segmentation and classification prostate cancer from MRI scans, in the development set

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.

• Publication year

  2023

• Venue

  Journal of Intelligent & Fuzzy Systems

• Publication date

  2023-05-19

• Fields of study

  Medicine, Computer Science

• Identifiers
  DOI 10.3233/JIFS-224274
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

• A Day Ahead Electrical Appliance Planning of Residential Units in a Smart Home Network Using ITS-BF Algorithm

  2022cited by this paper
• An integrated nomogram combining deep learning, Prostate Imaging–Reporting and Data System (PI-RADS) scoring, and clinical variables for identification of clinically significant prostate cancer on biparametric MRI: a retrospective multicentre study

  2021cited by this paper
• A Deep Learning Approach to Diagnostic Classification of Prostate Cancer Using Pathology–Radiology Fusion

  2021cited by this paper
• Effect of Dataset Size and Train/Test Split Ratios in QSAR/QSPR Multiclass Classification

  2021cited by this paper
• Self-transfer learning via patches: A prostate cancer triage approach based on bi-parametric MRI

  2021cited by this paper
• Anisotropic 3D Multi-Stream CNN for Accurate Prostate Segmentation from Multi-Planar MRI

  2020cited by this paper
• Agricultural Greenhouses Detection in High-Resolution Satellite Images Based on Convolutional Neural Networks: Comparison of Faster R-CNN, YOLO v3 and SSD

  2020influential reference
• A Closer Look at Accuracy vs. Robustness

  2020cited by this paper
• T1 and T2 MR fingerprinting measurements of prostate cancer and prostatitis correlate with deep learning–derived estimates of epithelium, lumen, and stromal composition on corresponding whole mount histopathology

  2020cited by this paper
• Automated Classification of Significant Prostate Cancer on MRI: A Systematic Review on the Performance of Machine Learning Applications

  2020cited by this paper
• Energy management strategy for solid‐state transformer‐based solar charging station for electric vehicles in smart grids

  2020cited by this paper
• Short-Channel Effects Improvement of Carbon Nanotube Field Effect Transistors

  2020cited by this paper
• RadioGraphics Update: PI-RADS Version 2.1-A Pictorial Update.

  2020influential reference
• Magnetic resonance imaging diagnosis of prostate cancer: promise and caution

  2019cited by this paper
• A survey on Image Data Augmentation for Deep Learning

  2019cited by this paper
• Prostate Cancer Detection using Deep Convolutional Neural Networks

  2019cited by this paper
• Faster R-CNN for marine organisms detection and recognition using data augmentation

  2019cited by this paper
• Using Pre-Training Can Improve Model Robustness and Uncertainty

  2019cited by this paper
• Multi-objective optimization based robust scheduling of electric vehicles aggregator

  2019cited by this paper
• Contemporary approach to active surveillance for favorable risk prostate cancer

  2018cited by this paper
• Computer‐aided diagnosis of prostate cancer on magnetic resonance imaging using a convolutional neural network algorithm

  2018cited by this paper
• Data augmentation for improving deep learning in image classification problem

  2018cited by this paper
• Convolutional neural networks: an overview and application in radiology

  2018cited by this paper
• MRI imaging texture features in prostate lesions classification

  2017cited by this paper
• MR-T2-weighted signal intensity: a new imaging biomarker of prostate cancer aggressiveness

  2016cited by this paper
• Computer-aided Detection of Prostate Cancer with MRI: Technology and Applications.

  2016cited by this paper
• Image contrast enhancement using histogram equalization with maximum intensity coverage

  2016influential reference
• Radiomics: Images Are More than Pictures, They Are Data

  2015cited by this paper
• Deep Residual Learning for Image Recognition

  2015cited by this paper
• Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks

  2015cited by this paper
• How much data is needed to train a medical image deep learning system to achieve necessary high accuracy

  2015cited by this paper
• Kernel-Based Learning From Both Qualitative and Quantitative Labels: Application to Prostate Cancer Diagnosis Based on Multiparametric MR Imaging

  2014cited by this paper
• Going deeper with convolutions

  2014cited by this paper
• Final Gleason Score Prediction Using Discriminant Analysis and Support Vector Machine Based on Preoperative Multiparametric MR Imaging of Prostate Cancer at 3T

  2014cited by this paper
• Computer-Aided Detection of Prostate Cancer in MRI

  2014cited by this paper
• Quantitative analysis of multiparametric prostate MR images: differentiation between prostate cancer and normal tissue and correlation with Gleason score--a computer-aided diagnosis development study.

  2013cited by this paper
• Upgrading and downgrading of prostate cancer from biopsy to radical prostatectomy: incidence and predictive factors using the modified Gleason grading system and factoring in tertiary grades.

  2012cited by this paper
• Enhancement of Images using Morphological Transformation

  2012cited by this paper
• ImageNet classification with deep convolutional neural networks

  2012cited by this paper
• Morphology Approach in Image Processing

  2012influential reference
• Fast Almost-Gaussian Filtering

  2010influential reference
• Image Database

  2009cited by this paper
• Classification of Opinions with Non-affective Adverbs and Adjectives

  2009cited by this paper

• Segmentation and Classification of Skin Cancer Diseases Based on Deep Learning: Challenges and Future Directions

  2025cites this paper
• A Classifier Model Using Fine-Tuned Convolutional Neural Network and Transfer Learning Approaches for Prostate Cancer Detection

  2024cites this paper