Matches in SemOpenAlex for { <https://semopenalex.org/work/W3012206005> ?p ?o ?g. }
Showing items 1 to 87 of
87
with 100 items per page.
- W3012206005 endingPage "114" @default.
- W3012206005 startingPage "99" @default.
- W3012206005 abstract "This chapter explicates deep learning algorithms for healthcare opportunities. Deep Learning is a group of neural network algorithms and learns from various levels of representation and abstraction to aid in the data interpretation. Since the datasets get bigger, computers become more powerful, and the training of the datasets (images or numeric) gets much easier and the results achieved using deep learning are better. In contrast to machine-learning algorithms that rely on large amounts of labelled data, human cognition can find structure in unlabeled data, a technique known as unsupervised learning. It was noted that using deep learning algorithms on the dataset will reduce the number of unnecessary biopsies in future. In this chapter, the authors study deep learning algorithms to diagnose diabetic retinopathy retinal images and training a convolution neural network (CNN) algorithm to identify object tumors from a large set of brain tumor images." @default.
- W3012206005 created "2020-03-23" @default.
- W3012206005 creator A5019115621 @default.
- W3012206005 creator A5028165285 @default.
- W3012206005 date "2020-01-01" @default.
- W3012206005 modified "2023-09-24" @default.
- W3012206005 title "Disease Diagnosis and Treatment Using Deep Learning Algorithms for the Healthcare System" @default.
- W3012206005 cites W1964391197 @default.
- W3012206005 cites W2022985331 @default.
- W3012206005 cites W2097117768 @default.
- W3012206005 cites W2128552241 @default.
- W3012206005 cites W2205836001 @default.
- W3012206005 cites W2533800772 @default.
- W3012206005 cites W2557738935 @default.
- W3012206005 cites W2592929672 @default.
- W3012206005 cites W2664267452 @default.
- W3012206005 cites W2758062365 @default.
- W3012206005 cites W2777014094 @default.
- W3012206005 cites W2800159852 @default.
- W3012206005 cites W2853673972 @default.
- W3012206005 cites W2887196013 @default.
- W3012206005 cites W2889042945 @default.
- W3012206005 cites W2911440706 @default.
- W3012206005 cites W3105282616 @default.
- W3012206005 doi "https://doi.org/10.4018/978-1-7998-2101-4.ch007" @default.
- W3012206005 hasPublicationYear "2020" @default.
- W3012206005 type Work @default.
- W3012206005 sameAs 3012206005 @default.
- W3012206005 citedByCount "4" @default.
- W3012206005 countsByYear W30122060052020 @default.
- W3012206005 countsByYear W30122060052023 @default.
- W3012206005 crossrefType "book-chapter" @default.
- W3012206005 hasAuthorship W3012206005A5019115621 @default.
- W3012206005 hasAuthorship W3012206005A5028165285 @default.
- W3012206005 hasConcept C108583219 @default.
- W3012206005 hasConcept C111472728 @default.
- W3012206005 hasConcept C11413529 @default.
- W3012206005 hasConcept C119857082 @default.
- W3012206005 hasConcept C124304363 @default.
- W3012206005 hasConcept C138885662 @default.
- W3012206005 hasConcept C153180895 @default.
- W3012206005 hasConcept C154945302 @default.
- W3012206005 hasConcept C177264268 @default.
- W3012206005 hasConcept C17744445 @default.
- W3012206005 hasConcept C199360897 @default.
- W3012206005 hasConcept C199539241 @default.
- W3012206005 hasConcept C2776359362 @default.
- W3012206005 hasConcept C41008148 @default.
- W3012206005 hasConcept C8038995 @default.
- W3012206005 hasConcept C81363708 @default.
- W3012206005 hasConcept C94625758 @default.
- W3012206005 hasConceptScore W3012206005C108583219 @default.
- W3012206005 hasConceptScore W3012206005C111472728 @default.
- W3012206005 hasConceptScore W3012206005C11413529 @default.
- W3012206005 hasConceptScore W3012206005C119857082 @default.
- W3012206005 hasConceptScore W3012206005C124304363 @default.
- W3012206005 hasConceptScore W3012206005C138885662 @default.
- W3012206005 hasConceptScore W3012206005C153180895 @default.
- W3012206005 hasConceptScore W3012206005C154945302 @default.
- W3012206005 hasConceptScore W3012206005C177264268 @default.
- W3012206005 hasConceptScore W3012206005C17744445 @default.
- W3012206005 hasConceptScore W3012206005C199360897 @default.
- W3012206005 hasConceptScore W3012206005C199539241 @default.
- W3012206005 hasConceptScore W3012206005C2776359362 @default.
- W3012206005 hasConceptScore W3012206005C41008148 @default.
- W3012206005 hasConceptScore W3012206005C8038995 @default.
- W3012206005 hasConceptScore W3012206005C81363708 @default.
- W3012206005 hasConceptScore W3012206005C94625758 @default.
- W3012206005 hasLocation W30122060051 @default.
- W3012206005 hasOpenAccess W3012206005 @default.
- W3012206005 hasPrimaryLocation W30122060051 @default.
- W3012206005 hasRelatedWork W10321813 @default.
- W3012206005 hasRelatedWork W11023528 @default.
- W3012206005 hasRelatedWork W11297145 @default.
- W3012206005 hasRelatedWork W12428677 @default.
- W3012206005 hasRelatedWork W14024944 @default.
- W3012206005 hasRelatedWork W1472067 @default.
- W3012206005 hasRelatedWork W6479499 @default.
- W3012206005 hasRelatedWork W6533109 @default.
- W3012206005 hasRelatedWork W8394581 @default.
- W3012206005 hasRelatedWork W959509 @default.
- W3012206005 isParatext "false" @default.
- W3012206005 isRetracted "false" @default.
- W3012206005 magId "3012206005" @default.
- W3012206005 workType "book-chapter" @default.