Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Search research articles

Related Experiment Video

Updated: May 22, 2026

Deep Neural Networks for Image-Based Dietary Assessment

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

FGOOPNet: A Fuzzy Deep Learning Approach for Mammographic Breast Cancer Classification

Ho Dat Tran^1,2, Anh Cang Phan², Thuong Cang Phan¹

¹Can Tho University, Can Tho, Vietnam.

Current Medical Imaging

|May 21, 2026

Summary

This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

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

Sort by

Same journal

A simple method for locating and calibrating the keyhole for the retrosigmoid approach using CT plain scans.

Current medical imaging·2026

Same journal

Multimodal Imaging of a Giant Ovarian Mature Cystic Teratoma Featuring the Floating Ball Sign: A Case Report.

Current medical imaging·2026

Same journal

Accurate Segmentation and Three-dimensional Reconstruction Algorithm of Spinal Cord Injury Lesions Based on Multimodal Magnetic Resonance Imaging.

Current medical imaging·2026

Same journal

A Comprehensive Review of Radiomics in Pulmonary Nodule Management: Clinical Applications and Standardization Dilemmas.

Current medical imaging·2026

Same journal

The Value of a Predictive Model Based on Multimodal Ultrasound Imaging Biomarkers Combined with Clinical Features in the Diagnosis of Thyroid Nodules.

Current medical imaging·2026

Same journal

The Prognostic and Mutational Characteristics of Multiple Early-stage Lung Cancers Manifesting as Subsolid Nodules.

Current medical imaging·2026

See all related articles

This study introduces FGOOPNet, a fuzzy deep ensemble model for improved mammographic breast cancer classification. FGOOPNet enhances diagnostic accuracy and transparency, aiding early detection efforts.

Area of Science:

Artificial Intelligence
Medical Imaging Analysis
Machine Learning for Healthcare

Background:

Breast cancer remains a global health threat, necessitating accurate early detection.
Current diagnostic challenges include interpretation inconsistencies and lesion assessment uncertainties in mammography.
FGOOPNet is proposed to improve mammographic breast cancer classification accuracy and decision transparency.

Purpose of the Study:

To develop an advanced deep ensemble model for enhanced mammographic breast cancer classification.
To improve diagnostic reliability by addressing uncertainties in lesion assessment.
To increase the transparency and interpretability of AI-driven diagnostic decisions.

Main Methods:

Integrated seven CNN architectures (InceptionV3, ResNet-50V2, ResNet-152, DenseNet-121, DenseNet-201, VGG19, Xception) using the Geometrically Optimum and Online Weighted Ensemble (GOOWE) strategy.

Keywords:

Breast cancer CAD systems Deep learning Ensemble learning Fuzzy Gaussian membership GOOWE Mammography

Related Experiment Videos

Last Updated: May 22, 2026

Deep Neural Networks for Image-Based Dietary Assessment

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

Employed a Gaussian-based fuzzy inference module to refine decision boundaries and quantify predictive uncertainty.

Evaluated on a hybrid dataset (VinDr-Mammo and private cohort) using accuracy, F1-score, and robustness metrics.

Main Results:

FGOOPNet achieved 98.4% accuracy and balanced F1-scores for benign and malignant classes.
Outperformed individual CNN models and conventional ensemble methods.
The fuzzy inference layer improved reliability by filtering low-confidence predictions and reducing misclassifications in borderline cases.

Conclusions:

The combination of GOOWE weighting and fuzzy Gaussian reasoning significantly enhances predictive performance and interpretability in mammographic analysis.
FGOOPNet provides an uncertainty-aware mechanism to overcome limitations of traditional CAD systems, especially in complex or ambiguous regions.
FGOOPNet presents a robust, explainable solution with potential for clinical integration in decision-support workflows.