A novel diagnostic framework for breast cancer: Combining deep learning with mammogram-DBT feature fusion

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F25%3A10256889" target="_blank" >RIV/61989100:27240/25:10256889 - isvavai.cz</a>

Result on the web

<a href="https://www.sciencedirect.com/science/article/pii/S2590123024020796?via%3Dihub" target="_blank" >https://www.sciencedirect.com/science/article/pii/S2590123024020796?via%3Dihub</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.rineng.2024.103836" target="_blank" >10.1016/j.rineng.2024.103836</a>

Result language

angličtina

Original language name

A novel diagnostic framework for breast cancer: Combining deep learning with mammogram-DBT feature fusion

Original language description

Background and motivation: Breast cancer detection remains a critical challenge in medical imaging due to the complexity of tumor features and variability in breast tissue. Conventional mammography struggles with dense tissues, leading to missed diagnoses. Digital Breast Tomosynthesis (DBT) offers improved 3D imaging but brings significant computational burdens. This study proposes a novel framework using the Fully Elman Neural Network (FENN) with feature fusion to enhance the accuracy and reliability of breast cancer diagnosis. Materials and methods: Mammogram images from the CBIS-DDSM dataset and DBT images from the BreastCancer-Screening-DBT dataset were used. The preprocessing step involved Extended-Tuned Adaptive Frost Filtering (Ext-AFF) to enhance image quality by reducing noise. Feature extraction was performed using Disentangled Variational Autoencoder (D-VAE), capturing critical texture features. These features were fused using Deep Generalized Canonical Correlation Analysis (Dg-CCA) to maximize feature correlation across modalities. Finally, a Fully Elman Neural Network was employed for classification, distinguishing between benign, malignant, biopsy-proven cancer, and normal tissues. Results: The proposed FENN-based framework achieved superior classification performance compared to existing methods. Key metrics such as accuracy, sensitivity, specificity, and Matthew&apos;s correlation coefficient (MCC) demonstrated significant improvements. The fusion of mammogram and DBT images led to enhanced discriminative power, reducing false positives and negatives across various breast cancer classes. Discussion and conclusion: The integration of mammogram and DBT image data with advanced machine learning techniques, such as D-VAE and FENN, enhances diagnostic precision. The proposed framework shows promise for improving clinical decision-making in breast cancer screening by overcoming the limitations of traditional imaging methods. The system&apos;s ability to handle complex interdependencies in imaging data offers substantial potential for earlier and more accurate diagnosis. Future directions: Future research will focus on real-time clinical deployment of the framework, incorporating real-time image acquisition and analysis for faster diagnoses. Additionally, scaling the system for large datasets with varying image quality will further validate its robustness and applicability in diverse clinical environments.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

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Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2025

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Results in Engineering

ISSN

2590-1230

e-ISSN

2590-1230

Volume of the periodical

25

Issue of the periodical within the volume

Neuveden

Country of publishing house

US - UNITED STATES

Number of pages

16

Pages from-to

nestránkováno

UT code for WoS article

001402224500001

EID of the result in the Scopus database

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