Integrating hybrid transfer learning with attention-enhanced deep learning models to improve breast cancer diagnosis

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18450%2F24%3A50021449" target="_blank" >RIV/62690094:18450/24:50021449 - isvavai.cz</a>

Result on the web

<a href="https://peerj.com/articles/cs-1850/" target="_blank" >https://peerj.com/articles/cs-1850/</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.7717/peerj-cs.1850" target="_blank" >10.7717/peerj-cs.1850</a>

Result language

angličtina

Original language name

Integrating hybrid transfer learning with attention-enhanced deep learning models to improve breast cancer diagnosis

Original language description

Cancer, with its high fatality rate, instills fear in countless individuals worldwide. However, effective diagnosis and treatment can often lead to a successful cure. Computer -assisted diagnostics, especially in the context of deep learning, have become prominent methods for primary screening of various diseases, including cancer. Deep learning, an artificial intelligence technique that enables computers to reason like humans, has recently gained significant attention. This study focuses on training a deep neural network to predict breast cancer. With the advancements in medical imaging technologies such as X-ray, magnetic resonance imaging (MRI), and computed tomography (CT) scans, deep learning has become essential in analyzing and managing extensive image datasets. The objective of this research is to propose a deep -learning model for the identification and categorization of breast tumors. The system&apos;s performance was evaluated using the breast cancer identification (BreakHis) classification datasets from the Kaggle repository and the Wisconsin Breast Cancer Dataset (WBC) from the UCI repository. The study&apos;s findings demonstrated an impressive accuracy rate of 100%, surpassing other state-of-the-art approaches. The suggested model was thoroughly evaluated using F1 -score, recall, precision, and accuracy metrics on the WBC dataset. Training, validation, and testing were conducted using pre-processed datasets, leading to remarkable results of 99.8% recall rate, 99.06% F1 -score, and 100% accuracy rate on the BreakHis dataset. Similarly, on the WBC dataset, the model achieved a 99% accuracy rate, a 98.7% recall rate, and a 99.03% F1 -score. These outcomes highlight the potential of deep learning models in accurately diagnosing breast cancer. Based on our research, it is evident that the proposed system outperforms existing approaches in this field.

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<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2024

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

10

Issue of the periodical within the volume

February

Country of publishing house

GB - UNITED KINGDOM

Number of pages

24

Pages from-to

"Article Number: 1850"

UT code for WoS article

001174202200006

EID of the result in the Scopus database

2-s2.0-85190251696