Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Imaging Studies IV: Magnetic Resonance Imaging01:27

Imaging Studies IV: Magnetic Resonance Imaging

55
Introduction:Magnetic Resonance Imaging, or MRI, can include a specialized imaging technique of the urinary system known as Magnetic Resonance Urography (MRU). This radiation-free technique uses strong magnetic fields and radio waves to produce detailed images with the help of a computer. MRU is particularly effective for visualizing fluid-filled structures like the kidneys, ureters, and bladder.Applications of MRI in the Genitourinary SystemKidneys and Ureters: MRI detects tumors, cysts,...
55
Imaging Studies I: Kidney, Ureter, and Bladder Studies01:28

Imaging Studies I: Kidney, Ureter, and Bladder Studies

42
Kidney, Ureter, and Bladder (KUB) StudiesKidney, Ureter, and Bladder (KUB) studies are standard diagnostic imaging procedures used to assess the anatomy of the urinary system. They are commonly utilized for patients experiencing abdominal pain or urinary symptoms. By using a simple X-ray of the abdomen, KUB studies can reveal structural and pathological abnormalities within the kidneys, ureters, and bladder. These studies are particularly valuable in diagnosing kidney stones, urinary...
42

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

CT-Based Liver Segmentation for Liver Surgery: A Hybrid Approach Based on 3D U-Net-ELM Model.

Biomedicines·2026
Same author

An Attention-Enhanced RegNetY Framework for Detection and Classification of Vertical Misfit in Implant-Supported Restorations: A Retrospective Study.

Diagnostics (Basel, Switzerland)·2026
Same author

Automated Classification of Maxillary Sinus Ostium Patency Using a ConvNeXt-Tiny + DeiT Gated MLP-Based Hybrid Deep Learning Model: A Retrospective CBCT Study.

Diagnostics (Basel, Switzerland)·2026
Same author

Dense-MoE vs Lite-MoE: A Gating-Weight-Aware Pruning Framework for Unpaired Multimodal Breast Cancer Diagnosis.

Journal of imaging informatics in medicine·2026
Same author

Automated Early Detection of Skin Cancer Using a CNN-ViT-Attention-Based Hybrid Model.

Biomedicines·2026
Same author

MidFusionEfficientV2: Improving Ophthalmic Diagnosis with Mid-Level RGB-LBP Fusion and SE Attention.

Journal of clinical medicine·2026

Related Experiment Video

Updated: Sep 16, 2025

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

6.9K

Fusion-Based Deep Learning Approach for Renal Cell Carcinoma Subtype Detection Using Multi-Phasic MRI Data.

Gulhan Kilicarslan1, Dilber Cetintas2, Taner Tuncer3

  • 1Department of Radiology, Elazig Fethi Sekin City Hospital, Elazığ 23280, Turkey.

Diagnostics (Basel, Switzerland)
|July 12, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a deep learning model using multiphase MRI scans to accurately classify renal cell carcinoma (RCC) subtypes, aiding radiologists in diagnosis.

Keywords:
deep learningkidney tumormulti-phasic MRI datarenal cell carcinomasemantic segmentation

More Related Videos

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

2.9K
Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.3K

Related Experiment Videos

Last Updated: Sep 16, 2025

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

6.9K
Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

2.9K
Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.3K

Area of Science:

  • Radiology
  • Artificial Intelligence
  • Oncology

Background:

  • Renal cell carcinoma (RCC) diagnosis is challenging due to similar imaging features across tumor types.
  • Subjective visual assessments and interobserver variability introduce diagnostic uncertainties.

Purpose of the Study:

  • To develop a deep learning model for accurate RCC subtype classification.
  • To provide a decision support tool for radiologists using multiphase MRI data.

Main Methods:

  • A hybrid deep learning model integrating T2, arterial (A), and venous (V) MRI phases was developed.
  • The model involved five steps: ROI selection, preprocessing, augmentation, feature extraction, and classification.
  • Support Vector Machine (SVM) was used for classification.

Main Results:

  • The model achieved 90% accuracy in classifying 1275 multiphase MRI images.
  • The hybrid approach demonstrated effective RCC subtype identification.

Conclusions:

  • Integrating multiphase MRI data with deep learning significantly improves RCC subtype classification.
  • The proposed model enhances clinical decision support for RCC diagnosis.