FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4385612985> ?p ?o ?g. }

• W4385612985 endingPage "101317" @default.
• W4385612985 startingPage "101317" @default.
• W4385612985 abstract "Deep learning has been widely applied in breast cancer screening to analyze images obtained from X-rays, ultrasound, magnetic resonances, and biopsies. This study suggests that deep learning can also be used to prescreen for cancer by analyzing heterogeneous data obtained from demographic and anthropometric information from patients, biological markers from routine blood samples, and relative risks from meta-analysis and international databases. In this document, feature selection is applied to a database of 64 women diagnosed with breast cancer and a counterfactual group of 52 healthy women, to identify the best predictors of cancer prescreening. The best predictors are used in k-fold Monte Carlo cross-validation experiments that compare deep learning against machine learning. The results indicate that a deep learning architecture that is fine-tuned using feature selection has the lowest false negative rate (i.e., the lowest Type II errors) and can effectively distinguish between patients with and without cancer. Consequently, deep learning—compared to traditional machine learning—promotes a more accurate detection of malignancies, hence reducing the risk of increased tumor size and cancer spreading to nearby or distant lymph nodes, tissues, or organs, due to late detection. Additionally, compared to machine learning, deep learning has the lowest uncertainty in its predictions, as indicated by the lowest standard deviation of its performance metrics. These findings indicate that deep learning algorithms applied to cancer prescreening offer a radiation-free, non-invasive, and an affordable complement to screening methods based on imagery. The implementation of deep learning algorithms in cancer prescreening helps to identify individuals who may require imaging-based screening, can encourage self-examination, and decreases the psychological drawbacks associated with false positives in cancer screening, ultimately leading to earlier detection of malignancy and reducing the healthcare and societal burden associated with cancer treatment." @default.
• W4385612985 created "2023-08-07" @default.
• W4385612985 creator A5020940103 @default.
• W4385612985 creator A5059228829 @default.
• W4385612985 date "2023-01-01" @default.
• W4385612985 modified "2023-09-25" @default.
• W4385612985 title "Deep learning algorithms for the early detection of breast cancer: A comparative study with traditional machine learning" @default.
• W4385612985 cites W1546780605 @default.
• W4385612985 cites W1573852501 @default.
• W4385612985 cites W1964576764 @default.
• W4385612985 cites W1975665312 @default.
• W4385612985 cites W2014018089 @default.
• W4385612985 cites W2055576561 @default.
• W4385612985 cites W2131987814 @default.
• W4385612985 cites W2145695837 @default.
• W4385612985 cites W2163905093 @default.
• W4385612985 cites W2164822601 @default.
• W4385612985 cites W2284497682 @default.
• W4385612985 cites W2309472119 @default.
• W4385612985 cites W2475596014 @default.
• W4385612985 cites W2533800772 @default.
• W4385612985 cites W2562498401 @default.
• W4385612985 cites W2764240208 @default.
• W4385612985 cites W2782929519 @default.
• W4385612985 cites W2799441468 @default.
• W4385612985 cites W2802914200 @default.
• W4385612985 cites W2885969267 @default.
• W4385612985 cites W2914540990 @default.
• W4385612985 cites W2917830189 @default.
• W4385612985 cites W2920435205 @default.
• W4385612985 cites W2944016032 @default.
• W4385612985 cites W2969326038 @default.
• W4385612985 cites W2977635842 @default.
• W4385612985 cites W2989693433 @default.
• W4385612985 cites W3000982932 @default.
• W4385612985 cites W3023402959 @default.
• W4385612985 cites W3036882247 @default.
• W4385612985 cites W3083832252 @default.
• W4385612985 cites W3092849554 @default.
• W4385612985 cites W3099458507 @default.
• W4385612985 cites W3112701542 @default.
• W4385612985 cites W3129087662 @default.
• W4385612985 cites W3144321749 @default.
• W4385612985 cites W3160844436 @default.
• W4385612985 cites W3186745756 @default.
• W4385612985 cites W4200483382 @default.
• W4385612985 cites W4206462383 @default.
• W4385612985 cites W4234698323 @default.
• W4385612985 cites W4293276539 @default.
• W4385612985 doi "https://doi.org/10.1016/j.imu.2023.101317" @default.
• W4385612985 hasPublicationYear "2023" @default.
• W4385612985 type Work @default.
• W4385612985 citedByCount "0" @default.
• W4385612985 crossrefType "journal-article" @default.
• W4385612985 hasAuthorship W4385612985A5020940103 @default.
• W4385612985 hasAuthorship W4385612985A5059228829 @default.
• W4385612985 hasBestOaLocation W43856129851 @default.
• W4385612985 hasConcept C108583219 @default.
• W4385612985 hasConcept C11413529 @default.
• W4385612985 hasConcept C119857082 @default.
• W4385612985 hasConcept C121608353 @default.
• W4385612985 hasConcept C126322002 @default.
• W4385612985 hasConcept C148483581 @default.
• W4385612985 hasConcept C154945302 @default.
• W4385612985 hasConcept C41008148 @default.
• W4385612985 hasConcept C530470458 @default.
• W4385612985 hasConcept C71924100 @default.
• W4385612985 hasConceptScore W4385612985C108583219 @default.
• W4385612985 hasConceptScore W4385612985C11413529 @default.
• W4385612985 hasConceptScore W4385612985C119857082 @default.
• W4385612985 hasConceptScore W4385612985C121608353 @default.
• W4385612985 hasConceptScore W4385612985C126322002 @default.
• W4385612985 hasConceptScore W4385612985C148483581 @default.
• W4385612985 hasConceptScore W4385612985C154945302 @default.
• W4385612985 hasConceptScore W4385612985C41008148 @default.
• W4385612985 hasConceptScore W4385612985C530470458 @default.
• W4385612985 hasConceptScore W4385612985C71924100 @default.
• W4385612985 hasLocation W43856129851 @default.
• W4385612985 hasOpenAccess W4385612985 @default.
• W4385612985 hasPrimaryLocation W43856129851 @default.
• W4385612985 hasRelatedWork W2795261237 @default.
• W4385612985 hasRelatedWork W3014300295 @default.
• W4385612985 hasRelatedWork W3164822677 @default.
• W4385612985 hasRelatedWork W4223943233 @default.
• W4385612985 hasRelatedWork W4225161397 @default.
• W4385612985 hasRelatedWork W4312200629 @default.
• W4385612985 hasRelatedWork W4360585206 @default.
• W4385612985 hasRelatedWork W4364306694 @default.
• W4385612985 hasRelatedWork W4380075502 @default.
• W4385612985 hasRelatedWork W4380086463 @default.
• W4385612985 hasVolume "41" @default.
• W4385612985 isParatext "false" @default.
• W4385612985 isRetracted "false" @default.
• W4385612985 workType "article" @default.