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

The Development of a Syringe-Based Insulin Applicator Using a Biodesign-Based Methodology.

Biomimetics (Basel, Switzerland)·2026
Same author

Peripheral thermoregulation patterns of stress and relaxation assessed via infrared thermography and linear discriminant analysis.

Journal of thermal biology·2026
Same author

Purification, Structural Characterization, and Antibacterial Evaluation of Poly-γ-Glutamic Acid from <i>Bacillus subtilis</i>.

Polymers·2026
Same author

A stacked autoencoder-based aid system for severity degree classification of knee ligament rupture.

Computers in biology and medicine·2024
Same author

Automatic Segmentation of Facial Regions of Interest and Stress Detection Using Machine Learning.

Sensors (Basel, Switzerland)·2024
Same author

Short-Term Thermal Effect of Continuous Ultrasound from 3 MHz to 1 and 0.5 W/cm<sup>2</sup> Applied to Gastrocnemius Muscle.

Diagnostics (Basel, Switzerland)·2023
Same journal

Correction: Adeluola et al. Chemoprevention of 4-NQO-Induced Oral Cancer by the Combination of Resveratrol and EGCG: In Vivo, In Silico and In Vitro Studies. <i>Cancers</i> 2026, <i>18</i>, 1098.

Cancers·2026
Same journal

Correction: Peñalver et al. Guidelines for Diagnosis, Treatment, and Follow-Up of Patients with Follicular Lymphoma-Spanish Lymphoma Group (GELTAMO) 2026. <i>Cancers</i> 2026, <i>18</i>, 395.

Cancers·2026
Same journal

Correction: Accorsi Buttini et al. Development of a Simplified Geriatric Score-4 (SGS-4) to Predict Outcomes After Allogeneic Hematopoietic Stem Cell Transplantation in Patients Aged over 50. <i>Cancers</i> 2025, <i>17</i>, 3278.

Cancers·2026
Same journal

Age-Stratified Long-Term Outcomes of Immune Checkpoint Inhibitors for Stage IV Melanoma and NSCLC in The Netherlands: A Population-Based Study.

Cancers·2026
Same journal

Targeting Ferroptosis in Glioblastoma: Molecular Mechanisms, Tumor Microenvironment, and Therapeutic Opportunities.

Cancers·2026
Same journal

Neoadjuvant Immunotherapy-Based Treatment Versus Chemotherapy Alone in Resectable Locally Advanced dMMR/MSI-H Gastric Cancer: A Real-World Study with Meta-Analysis.

Cancers·2026
See all related articles
  1. Home
  3. Diagnostic Strategies For Breast Cancer Detection: From Image Generation To Classification Strategies Using Artificial Intelligence Algorithms.
  1. Home
  3. Diagnostic Strategies For Breast Cancer Detection: From Image Generation To Classification Strategies Using Artificial Intelligence Algorithms.

Related Experiment Video

Detection and Isolation of Cancer in Prostate Biopsies Using Stimulated Raman Histology and Artificial Intelligence
08:05

Detection and Isolation of Cancer in Prostate Biopsies Using Stimulated Raman Histology and Artificial Intelligence

Published on: June 10, 2025

191

Diagnostic Strategies for Breast Cancer Detection: From Image Generation to Classification Strategies Using

Jesus A Basurto-Hurtado1,2, Irving A Cruz-Albarran1,2, Manuel Toledano-Ayala3

  • 1C.A. Mecatrónica, Facultad de Ingeniería, Campus San Juan del Río, Universidad Autónoma de Querétaro, Rio Moctezuma 249, San Cayetano, San Juan del Rio 76807, Mexico.

Cancers
|July 27, 2022

View abstract on PubMed

Summary
This summary is machine-generated.

This review covers image generation and processing for early breast cancer detection. Future methods should integrate artificial intelligence for improved accuracy and reliability in diagnosing malignant lesions.

Keywords:
artificial intelligencebreast cancerimage processingmagnetic resonancemammographythermographyultrasound

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

6.9K
Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging
15:48

Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging

Published on: December 15, 2014

22.6K

Related Experiment Videos

Detection and Isolation of Cancer in Prostate Biopsies Using Stimulated Raman Histology and Artificial Intelligence
08:05

Detection and Isolation of Cancer in Prostate Biopsies Using Stimulated Raman Histology and Artificial Intelligence

Published on: June 10, 2025

191
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
Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging
15:48

Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging

Published on: December 15, 2014

22.6K

Area of Science:

  • Medical Imaging
  • Oncology
  • Artificial Intelligence

Background:

  • Breast cancer is a leading cause of death in women globally, necessitating early diagnosis for improved survival rates.
  • Current research often focuses on specific aspects of breast cancer detection, lacking a holistic view from image generation to interpretation.

Purpose of the Study:

  • To provide a comprehensive state-of-the-art review of image generation and processing techniques for breast cancer detection.
  • To discuss potential candidates for image generation and processing in breast cancer diagnostics.

Main Methods:

  • Systematic literature review of image generation techniques.
  • Analysis of image processing methodologies for breast cancer detection.
  • Discussion of artificial intelligence integration in diagnostic workflows.

Main Results:

  • Identified key image generation and processing techniques relevant to breast cancer detection.
  • Highlighted the need for integrated approaches combining AI and categorical data.
  • Discussed the potential for novel methodologies to enhance accuracy and reliability.

Conclusions:

  • A comprehensive understanding of image generation and processing is crucial for advancing breast cancer detection.
  • Future research should focus on integrating artificial intelligence with existing data for more precise and reliable diagnostic tools.
  • Mitigating misclassifications requires novel methodologies that ensure accuracy, precision, and reliability in breast cancer imaging analysis.