A hybrid deep learning approach with progressive cyclical CNN and firebug swarm optimization for breast cancer detection

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18450%2F25%3A50022689" target="_blank" >RIV/62690094:18450/25:50022689 - isvavai.cz</a>

Result on the web

<a href="https://peerj.com/articles/cs-3119/" target="_blank" >https://peerj.com/articles/cs-3119/</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.7717/peerj-cs.3119" target="_blank" >10.7717/peerj-cs.3119</a>

Result language

angličtina

Original language name

A hybrid deep learning approach with progressive cyclical CNN and firebug swarm optimization for breast cancer detection

Original language description

The practice of diagnosing breast cancer retains its scope for improvement in medical imaging, where every correct and timely diagnosis enhances the survival rate of patients. This article presents an integrated approach utilizing patch-wise breast image segmentation, hybrid deep feature extraction, followed by progressive cyclical convolutional neural networks (P-CycCNN), and firebug swarm optimization (FSO) to enhance breast cancer detection. This method first incorporates image segmentation by patches to break down the mammography images into smaller patches, which are easier to focus on and allow for the extraction of more features to boost detection rates. Hybrid feature extraction combines convolutional neural network (CNN) features extracted from pre-trained models with handcrafted features that describe texture and shape, thereby enabling the model to grasp the nuances of both coarse and fine images comprehensively. The progressive cyclical CNN strategy incorporates cyclical, re-adjusted learning rates and a progressive training schedule to accelerate and enhance the model&apos;s convergence. FSO is introduced to adjust the hyperparameters of the CNN topology, including the learning rate and regularisation parameters, thereby enhancing training and feature-fusion processes. Evaluated on the Curated Breast Imaging Subset of the Digital Database for Screening Mammography (CBIS-DDSM) dataset, the proposed model achieved 98% test accuracy, 95% precision, 97.2% recall, 96% F1-score, and an AUC of 0.95, outperforming baseline CNN models by 4%-6% across key metrics. This approach holds great potential for enhancing detection systems in clinics, allowing earlier and more accurate detection of malignant lesions.

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

—

OECD FORD branch

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

Project

—

Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2025

Confidentiality

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

Name of the periodical

PeerJ Computer Science

ISSN

2376-5992

e-ISSN

2376-5992

Volume of the periodical

11

Issue of the periodical within the volume

September

Country of publishing house

GB - UNITED KINGDOM

Number of pages

30

Pages from-to

"Article Number: e3119"

UT code for WoS article

001591528600001

EID of the result in the Scopus database

2-s2.0-105028376891