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

You might also read

Related Articles

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

Sort by
Same author

AI-driven neuroanalytic modeling for mental health: multichannel CNN-based autism spectrum disorder detection via facial pattern analysis.

Frontiers in computational neuroscience·2026
Same author

An interpretable deep concatenated architecture for osteoporosis detection using enhanced knee radiographs.

Frontiers in medicine·2026
Same author

Demystifying Artificial Intelligence: A Systematic Review of Explainable Artificial Intelligence in Medical Imaging.

Sensors (Basel, Switzerland)·2026
Same author

Which Strategy When? Designing an Adaptive Search System for Virtual Reality.

IEEE transactions on visualization and computer graphics·2026
Same author

Improving road safety in smart cities using machine learning techniques.

Scientific reports·2026
Same author

Advancing workpiece dimension measurement: Integrating AI-based edge detection with machine vision and coordinate measuring systems.

PloS one·2026

Related Experiment Video

Updated: Jun 27, 2025

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

2.7K

Revolutionizing tumor detection and classification in multimodality imaging based on deep learning approaches:

Dildar Hussain1, Mohammed A Al-Masni1, Muhammad Aslam1

  • 1Department of Artificial Intelligence and Data Science, Sejong University, Seoul, Korea.

Journal of X-Ray Science and Technology
|May 3, 2024
PubMed
Summary

Deep learning (DL) significantly enhances tumor detection and classification in multimodal medical imaging (MMI). This review explores DL advancements, challenges, and future directions for improved clinical diagnosis and prognosis.

Keywords:
CTGANsMRIMultimodal medical imagePETdeep learningfusionimage analysissegmentation

More Related Videos

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

6.8K
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

8.2K

Related Experiment Videos

Last Updated: Jun 27, 2025

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

2.7K
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

6.8K
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

8.2K

Area of Science:

  • Medical Imaging Analysis
  • Artificial Intelligence in Oncology

Background:

  • Deep learning (DL) revolutionizes tumor detection and classification in medical imaging.
  • Multimodal medical imaging (MMI) offers precision in diagnosis, treatment, and progression tracking.

Purpose of the Study:

  • To comprehensively review DL methods for tumor detection and classification in MMI.
  • To provide insights into advancements, limitations, and challenges in the field.

Main Methods:

  • Systematic literature analysis of DL studies for tumor detection and classification.
  • Examination of methodologies including Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs).
  • Focus on the integration of multimodality imaging for enhanced accuracy.

Main Results:

  • Survey of DL-based MMI evaluation methods for tumor detection and classification.
  • Discussion of various DL approaches (CNNs, YOLO, Siamese Networks, etc.) across PET-MRI, PET-CT, and SPECT-CT.
  • Highlighting advancements in Fusion-Based, Attention-Based, and Generative Adversarial Networks.

Conclusions:

  • DL approaches are effective for tumor detection and classification in MMI.
  • DL holds significant potential to address challenges in MMI analysis.
  • Findings have implications for advancing clinical practice in oncology.