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Related Experiment Video

Updated: Feb 28, 2026

A Multimodal Imaging Framework to Advance Phenotyping of Living Label-free Breast Cancer Cells
10:37

A Multimodal Imaging Framework to Advance Phenotyping of Living Label-free Breast Cancer Cells

Published on: August 22, 2025

1.3K

An Effective Breast Cancer Classification System Using Multiple Feature Extraction Techniques with Multi-scale

Atul Kumar Ramotra1, Goldi Chandrapal Jarbais2

  • 1Department of CSE-(AI & ML), ACE Engineering College, Ankushapur, Ghatkesar, Telangana, 501301, India. atulkumar@aceec.ac.in.

Journal of Imaging Informatics in Medicine
|February 26, 2026
PubMed
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This study introduces an automated breast cancer detection system using feature fusion for improved accuracy. The novel framework achieves high performance in classifying breast cancer from mammography images.

Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Computer-Aided Diagnosis

Background:

  • Breast cancer is a leading cause of death in women, necessitating early detection.
  • Manual diagnosis is time-consuming, highlighting the need for automated systems.
  • Early breast cancer identification significantly reduces mortality rates.

Purpose of the Study:

  • To develop a novel framework for automated breast cancer classification using feature fusion.
  • To enhance the accuracy and efficiency of breast cancer detection.
  • To combine diverse features for robust classification.

Main Methods:

  • Utilized CBIS-DDSM and MIAS datasets, employing image augmentation and homomorphic filtering for pre-processing.
  • Extracted handcrafted (HOG, LBP), statistical (MPCA), textural, and deep (ViT) features.
Keywords:
Breast cancer classificationDeep featuresDeep learningFeature fusionHomomorphic filtering

Related Experiment Videos

Last Updated: Feb 28, 2026

A Multimodal Imaging Framework to Advance Phenotyping of Living Label-free Breast Cancer Cells
10:37

A Multimodal Imaging Framework to Advance Phenotyping of Living Label-free Breast Cancer Cells

Published on: August 22, 2025

1.3K
  • Implemented a multi-scale attention-based feature fusion model with softmax classification.

    • Main Results:

    • Achieved 99.45% accuracy on the CBIS-DDSM dataset.
    • Attained 99.73% accuracy on the MIAS dataset.
    • Demonstrated superior performance compared to existing breast cancer detection techniques.

    Conclusions:

    • The proposed feature fusion framework significantly improves automated breast cancer classification accuracy.
    • This automated system offers a promising tool for early and reliable breast cancer detection.
    • The integration of diverse features enhances the robustness of the diagnostic model.