FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2891858760> ?p ?o ?g. }

• W2891858760 endingPage "381" @default.
• W2891858760 startingPage "373" @default.
• W2891858760 abstract "Application of deep-learning technology to skin cancer classification can potentially improve the sensitivity and specificity of skin cancer screening, but the number of training images required for such a system is thought to be extremely large.To determine whether deep-learning technology could be used to develop an efficient skin cancer classification system with a relatively small dataset of clinical images.A deep convolutional neural network (DCNN) was trained using a dataset of 4867 clinical images obtained from 1842 patients diagnosed with skin tumours at the University of Tsukuba Hospital from 2003 to 2016. The images consisted of 14 diagnoses, including both malignant and benign conditions. Its performance was tested against 13 board-certified dermatologists and nine dermatology trainees.The overall classification accuracy of the trained DCNN was 76·5%. The DCNN achieved 96·3% sensitivity (correctly classified malignant as malignant) and 89·5% specificity (correctly classified benign as benign). Although the accuracy of malignant or benign classification by the board-certified dermatologists was statistically higher than that of the dermatology trainees (85·3% ± 3·7% and 74·4% ± 6·8%, P < 0·01), the DCNN achieved even greater accuracy, as high as 92·4% ± 2·1% (P < 0·001).We have developed an efficient skin tumour classifier using a DCNN trained on a relatively small dataset. The DCNN classified images of skin tumours more accurately than board-certified dermatologists. Collectively, the current system may have capabilities for screening purposes in general medical practice, particularly because it requires only a single clinical image for classification." @default.
• W2891858760 created "2018-09-27" @default.
• W2891858760 creator A5004269034 @default.
• W2891858760 creator A5004908859 @default.
• W2891858760 creator A5018825942 @default.
• W2891858760 creator A5024200791 @default.
• W2891858760 creator A5029889888 @default.
• W2891858760 creator A5040895870 @default.
• W2891858760 creator A5051555588 @default.
• W2891858760 creator A5052168803 @default.
• W2891858760 creator A5057689148 @default.
• W2891858760 creator A5074629996 @default.
• W2891858760 date "2018-09-19" @default.
• W2891858760 modified "2023-10-10" @default.
• W2891858760 title "Deep‐learning‐based, computer‐aided classifier developed with a small dataset of clinical images surpasses board‐certified dermatologists in skin tumour diagnosis" @default.
• W2891858760 cites W1491251669 @default.
• W2891858760 cites W1529230314 @default.
• W2891858760 cites W1600772273 @default.
• W2891858760 cites W1949574066 @default.
• W2891858760 cites W1952697361 @default.
• W2891858760 cites W1967349671 @default.
• W2891858760 cites W1975502721 @default.
• W2891858760 cites W1983037324 @default.
• W2891858760 cites W1992108446 @default.
• W2891858760 cites W1998451602 @default.
• W2891858760 cites W2002475630 @default.
• W2891858760 cites W2006229201 @default.
• W2891858760 cites W2006607153 @default.
• W2891858760 cites W2007675231 @default.
• W2891858760 cites W2032275493 @default.
• W2891858760 cites W2034737120 @default.
• W2891858760 cites W2035247099 @default.
• W2891858760 cites W2040600853 @default.
• W2891858760 cites W2041914157 @default.
• W2891858760 cites W2045293884 @default.
• W2891858760 cites W2063044204 @default.
• W2891858760 cites W2073556702 @default.
• W2891858760 cites W2074317748 @default.
• W2891858760 cites W2101764630 @default.
• W2891858760 cites W2117539524 @default.
• W2891858760 cites W2138530019 @default.
• W2891858760 cites W2142595568 @default.
• W2891858760 cites W2256917630 @default.
• W2891858760 cites W2257979135 @default.
• W2891858760 cites W2412801665 @default.
• W2891858760 cites W2528425554 @default.
• W2891858760 cites W2581082771 @default.
• W2891858760 cites W2594235371 @default.
• W2891858760 cites W2623166637 @default.
• W2891858760 cites W2757940437 @default.
• W2891858760 cites W2766846498 @default.
• W2891858760 cites W2771419433 @default.
• W2891858760 cites W3123387168 @default.
• W2891858760 cites W2017743303 @default.
• W2891858760 doi "https://doi.org/10.1111/bjd.16924" @default.
• W2891858760 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/29953582" @default.
• W2891858760 hasPublicationYear "2018" @default.
• W2891858760 type Work @default.
• W2891858760 sameAs 2891858760 @default.
• W2891858760 citedByCount "193" @default.
• W2891858760 countsByYear W28918587602018 @default.
• W2891858760 countsByYear W28918587602019 @default.
• W2891858760 countsByYear W28918587602020 @default.
• W2891858760 countsByYear W28918587602021 @default.
• W2891858760 countsByYear W28918587602022 @default.
• W2891858760 countsByYear W28918587602023 @default.
• W2891858760 crossrefType "journal-article" @default.
• W2891858760 hasAuthorship W2891858760A5004269034 @default.
• W2891858760 hasAuthorship W2891858760A5004908859 @default.
• W2891858760 hasAuthorship W2891858760A5018825942 @default.
• W2891858760 hasAuthorship W2891858760A5024200791 @default.
• W2891858760 hasAuthorship W2891858760A5029889888 @default.
• W2891858760 hasAuthorship W2891858760A5040895870 @default.
• W2891858760 hasAuthorship W2891858760A5051555588 @default.
• W2891858760 hasAuthorship W2891858760A5052168803 @default.
• W2891858760 hasAuthorship W2891858760A5057689148 @default.
• W2891858760 hasAuthorship W2891858760A5074629996 @default.
• W2891858760 hasConcept C108583219 @default.
• W2891858760 hasConcept C121608353 @default.
• W2891858760 hasConcept C126322002 @default.
• W2891858760 hasConcept C126838900 @default.
• W2891858760 hasConcept C154945302 @default.
• W2891858760 hasConcept C17744445 @default.
• W2891858760 hasConcept C19527891 @default.
• W2891858760 hasConcept C199539241 @default.
• W2891858760 hasConcept C2777789703 @default.
• W2891858760 hasConcept C2779974597 @default.
• W2891858760 hasConcept C3020132585 @default.
• W2891858760 hasConcept C41008148 @default.
• W2891858760 hasConcept C46304622 @default.
• W2891858760 hasConcept C512399662 @default.
• W2891858760 hasConcept C534262118 @default.
• W2891858760 hasConcept C71924100 @default.
• W2891858760 hasConcept C81363708 @default.
• W2891858760 hasConcept C95623464 @default.
• W2891858760 hasConceptScore W2891858760C108583219 @default.
• W2891858760 hasConceptScore W2891858760C121608353 @default.
• W2891858760 hasConceptScore W2891858760C126322002 @default.

• next