Matches in SemOpenAlex for { <https://semopenalex.org/work/W4382678821> ?p ?o ?g. }
Showing items 1 to 95 of
95
with 100 items per page.
- W4382678821 endingPage "4667" @default.
- W4382678821 startingPage "4651" @default.
- W4382678821 abstract "Cancer is a devastating disease that has far-reaching effects on our culture and economy, in addition to the human lives it takes. Regarding budgetary responsibility, investing just in cancer treatment is not an option. Early diagnosis is a crucial part of the remedy that sometimes gets overlooked. Malignancy is often diagnosed and evaluated using Histopathology Images (HI), which are widely accepted as the gold standard in the field. Yet, even for experienced pathologists, analysing such images is challenging, which raises concerns of inter- and intra-observer variability. The analysis also requires a substantial investment of time and energy. One way that such an examination may be sped up is by making use of computer-assisted diagnostics devices. The purpose of this research is to create a comprehensive cancer detection system using images of breast and prostate histopathology stained with haematoxylin and eosin (H&E). Proposed here is work on improving colour normalisation methods, constructing an integrated model for nuclei segmentation and multiple objects overlap resolution, introducing and evaluating multi-level features for extracting relevant histopathological image and interpretable information, and developing classification algorithms for tasks such as cancer diagnosis, tumor identification, and tumor class labelling. Mini-Batch Stochastic Gradient Descent and Convolutional Neural Network which obtained statistical kappa value for breast cancer histopathology images shows a high degree of consistency in the classification task, with a range of 0.610.80 for benign and low grades and a range of 0.811.0 for medium and high rates. The Support Vector Machine (SVM), on the other hand, shows an almost perfect degree of consistency (0.811.0) across the several breast cancer picture classifications (benign, low, medium, and high)." @default.
- W4382678821 created "2023-07-01" @default.
- W4382678821 creator A5010900352 @default.
- W4382678821 creator A5055719956 @default.
- W4382678821 creator A5056483548 @default.
- W4382678821 creator A5073726146 @default.
- W4382678821 date "2023-08-24" @default.
- W4382678821 modified "2023-09-23" @default.
- W4382678821 title "Breast cancer detection using Histopathology Image with Mini-Batch Stochastic Gradient Descent and Convolutional Neural Network" @default.
- W4382678821 cites W2038335084 @default.
- W4382678821 cites W2116006589 @default.
- W4382678821 cites W2774980628 @default.
- W4382678821 cites W2898574163 @default.
- W4382678821 cites W2913819571 @default.
- W4382678821 cites W2946053491 @default.
- W4382678821 cites W2963716858 @default.
- W4382678821 cites W2973325025 @default.
- W4382678821 cites W2979078804 @default.
- W4382678821 cites W2982346646 @default.
- W4382678821 cites W2985347975 @default.
- W4382678821 cites W3005698181 @default.
- W4382678821 cites W3032835476 @default.
- W4382678821 cites W3093254401 @default.
- W4382678821 cites W3094910079 @default.
- W4382678821 cites W3108319156 @default.
- W4382678821 cites W3120006163 @default.
- W4382678821 cites W3125764491 @default.
- W4382678821 cites W3143013739 @default.
- W4382678821 cites W3182569057 @default.
- W4382678821 cites W3190151660 @default.
- W4382678821 cites W3205707234 @default.
- W4382678821 cites W3211713681 @default.
- W4382678821 cites W4220666790 @default.
- W4382678821 cites W4220918041 @default.
- W4382678821 doi "https://doi.org/10.3233/jifs-231480" @default.
- W4382678821 hasPublicationYear "2023" @default.
- W4382678821 type Work @default.
- W4382678821 citedByCount "0" @default.
- W4382678821 crossrefType "journal-article" @default.
- W4382678821 hasAuthorship W4382678821A5010900352 @default.
- W4382678821 hasAuthorship W4382678821A5055719956 @default.
- W4382678821 hasAuthorship W4382678821A5056483548 @default.
- W4382678821 hasAuthorship W4382678821A5073726146 @default.
- W4382678821 hasConcept C119857082 @default.
- W4382678821 hasConcept C121608353 @default.
- W4382678821 hasConcept C12267149 @default.
- W4382678821 hasConcept C124504099 @default.
- W4382678821 hasConcept C126322002 @default.
- W4382678821 hasConcept C142724271 @default.
- W4382678821 hasConcept C153180895 @default.
- W4382678821 hasConcept C154945302 @default.
- W4382678821 hasConcept C206688291 @default.
- W4382678821 hasConcept C41008148 @default.
- W4382678821 hasConcept C50644808 @default.
- W4382678821 hasConcept C530470458 @default.
- W4382678821 hasConcept C544855455 @default.
- W4382678821 hasConcept C71924100 @default.
- W4382678821 hasConcept C81363708 @default.
- W4382678821 hasConcept C89600930 @default.
- W4382678821 hasConceptScore W4382678821C119857082 @default.
- W4382678821 hasConceptScore W4382678821C121608353 @default.
- W4382678821 hasConceptScore W4382678821C12267149 @default.
- W4382678821 hasConceptScore W4382678821C124504099 @default.
- W4382678821 hasConceptScore W4382678821C126322002 @default.
- W4382678821 hasConceptScore W4382678821C142724271 @default.
- W4382678821 hasConceptScore W4382678821C153180895 @default.
- W4382678821 hasConceptScore W4382678821C154945302 @default.
- W4382678821 hasConceptScore W4382678821C206688291 @default.
- W4382678821 hasConceptScore W4382678821C41008148 @default.
- W4382678821 hasConceptScore W4382678821C50644808 @default.
- W4382678821 hasConceptScore W4382678821C530470458 @default.
- W4382678821 hasConceptScore W4382678821C544855455 @default.
- W4382678821 hasConceptScore W4382678821C71924100 @default.
- W4382678821 hasConceptScore W4382678821C81363708 @default.
- W4382678821 hasConceptScore W4382678821C89600930 @default.
- W4382678821 hasIssue "3" @default.
- W4382678821 hasLocation W43826788211 @default.
- W4382678821 hasOpenAccess W4382678821 @default.
- W4382678821 hasPrimaryLocation W43826788211 @default.
- W4382678821 hasRelatedWork W2041399278 @default.
- W4382678821 hasRelatedWork W2136184105 @default.
- W4382678821 hasRelatedWork W2160451891 @default.
- W4382678821 hasRelatedWork W2996933976 @default.
- W4382678821 hasRelatedWork W3013515612 @default.
- W4382678821 hasRelatedWork W3208266890 @default.
- W4382678821 hasRelatedWork W4287776258 @default.
- W4382678821 hasRelatedWork W2187500075 @default.
- W4382678821 hasRelatedWork W2345184372 @default.
- W4382678821 hasRelatedWork W2736898786 @default.
- W4382678821 hasVolume "45" @default.
- W4382678821 isParatext "false" @default.
- W4382678821 isRetracted "false" @default.
- W4382678821 workType "article" @default.