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 Experiment Video

Updated: Jan 1, 2026

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
13:44

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Published on: August 30, 2013

43.5K

Classification of Mammogram Images Using Multiscale all Convolutional Neural Network (MA-CNN).

S Akila Agnes1, J Anitha1, S Immanuel Alex Pandian2

  • 1Department of Computer Science and Engineering, Karunya Institute of Technology and Sciences, Coimbatore, India.

Journal of Medical Systems
|December 16, 2019
PubMed
Summary

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

UAV-based RGB and multispectral mango leaf disease detection with benchmarking of YOLOv5 to YOLOv10 and SeqOpt-optimised YOLOv8 for real-time edge deployment.

PloS one·2026
Same author

Letter to the Editor re: "Peak systolic velocity, not vein size, predicts abnormal sperm count in adolescent Tanner V patients with primary left varicocele".

Journal of pediatric urology·2026
Same author

A hybrid AIoT model for workplace stress prediction and intervention using social media platforms.

Discover mental health·2026
Same author

Optimizing vision care: Dual path network model in eye disease classification.

Computers in biology and medicine·2025
Same author

Enhancing performance of Parallel Hybrid Electric Vehicles using Powell's Artificial Bee Colony method.

Heliyon·2025
Same author

Attention deficit hyperactivity disorder (ADHD) detection for IoT based EEG signal.

Computer methods in biomechanics and biomedical engineering·2024

This study introduces a Multiscale All Convolutional Neural Network (MA-CNN) for early breast cancer detection. The MA-CNN model accurately classifies mammograms, improving diagnostic accuracy and aiding radiologists in identifying normal, malignant, and benign tumors.

Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Oncology

Background:

  • Breast cancer is a leading cause of cancer death in women globally.
  • Early diagnosis significantly improves survival rates and treatment efficacy.
  • Digital mammography is a key tool for early breast cancer detection.

Purpose of the Study:

  • To develop an effective deep learning model for automated breast cancer diagnosis.
  • To assist radiologists in accurately classifying mammogram images.
  • To improve the accuracy and efficiency of breast cancer detection using artificial intelligence.

Main Methods:

  • Development of a Multiscale All Convolutional Neural Network (MA-CNN).
  • Utilizing convolutional neural networks for feature extraction and classification.
Keywords:
Breast cancerComputer-aided detectionDeep convolutional neural networkFeature learningImage classification

More Related Videos

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

968

Related Experiment Videos

Last Updated: Jan 1, 2026

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
13:44

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Published on: August 30, 2013

43.5K
Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

968
  • Training and validation on the mini-MIAS mammographic dataset.

    • Main Results:

    • The MA-CNN model achieved high accuracy in classifying mammograms.
    • The system automatically categorized images into normal, malignant, and benign classes.
    • Achieved an overall sensitivity of 96% and an Area Under the Curve (AUC) of 0.99.

    Conclusions:

    • MA-CNN is a powerful tool for effective breast cancer diagnosis.
    • The multiscale approach enhances classification accuracy without compromising speed.
    • This AI-driven method aids in early and accurate detection of breast cancer.