Matches in SemOpenAlex for { <https://semopenalex.org/work/W3094060512> ?p ?o ?g. }
- W3094060512 endingPage "24951" @default.
- W3094060512 startingPage "24941" @default.
- W3094060512 abstract "Stroke is among the first pathologies that kill the most in the world, ranking second in deaths from illness. Each year, around 16 million people worldwide are victims of this disease, with approximately 38 % of cases brought to death. Computed Tomography (CT) is a super effective method to aid the medical diagnosis of stroke. But the analysis is subject to variations in the perception of specialists about its characteristics. Faced with the challenge of diagnosing stroke on CT images, this study proposes a fully automatic system based on Health of Things capable of classifying CT images of the skull through deep learning networks, classifying them into (Uninjured or Hemorrhagic stroke). After the image classification, Mask R-CNN segments the stroke through a learning transfer process, combined with machine learning methods. Our innovative method selected excellent results, both for classification with 100 % accuracy, and for segmentation in our best model (Mask + kNN) that reached 99.93% specificity and 99.73 accuracy, with segmentation time of 4.00 seconds, surpassing literature methods based on automatic models." @default.
- W3094060512 created "2020-10-29" @default.
- W3094060512 creator A5012656806 @default.
- W3094060512 creator A5043595651 @default.
- W3094060512 creator A5045093520 @default.
- W3094060512 creator A5048140802 @default.
- W3094060512 creator A5049651824 @default.
- W3094060512 creator A5059650150 @default.
- W3094060512 creator A5062266519 @default.
- W3094060512 creator A5070200818 @default.
- W3094060512 creator A5073951902 @default.
- W3094060512 creator A5077950662 @default.
- W3094060512 creator A5086921316 @default.
- W3094060512 date "2021-11-15" @default.
- W3094060512 modified "2023-10-17" @default.
- W3094060512 title "Deep Learning-Enhanced Internet of Medical Things to Analyze Brain CT Scans of Hemorrhagic Stroke Patients: A New Approach" @default.
- W3094060512 cites W1901361661 @default.
- W3094060512 cites W1909740415 @default.
- W3094060512 cites W2010991055 @default.
- W3094060512 cites W2082526668 @default.
- W3094060512 cites W2095421347 @default.
- W3094060512 cites W2095727900 @default.
- W3094060512 cites W2107878631 @default.
- W3094060512 cites W2112282447 @default.
- W3094060512 cites W2125283600 @default.
- W3094060512 cites W2133025798 @default.
- W3094060512 cites W2136132422 @default.
- W3094060512 cites W2253429366 @default.
- W3094060512 cites W2441649867 @default.
- W3094060512 cites W2497458316 @default.
- W3094060512 cites W2512934357 @default.
- W3094060512 cites W2557728737 @default.
- W3094060512 cites W2586433286 @default.
- W3094060512 cites W2588463901 @default.
- W3094060512 cites W2621028221 @default.
- W3094060512 cites W2783677152 @default.
- W3094060512 cites W2791187255 @default.
- W3094060512 cites W2803200748 @default.
- W3094060512 cites W2888989106 @default.
- W3094060512 cites W2897064646 @default.
- W3094060512 cites W2897366887 @default.
- W3094060512 cites W2911142008 @default.
- W3094060512 cites W2911722481 @default.
- W3094060512 cites W2911964244 @default.
- W3094060512 cites W2913705661 @default.
- W3094060512 cites W2935806806 @default.
- W3094060512 cites W2944278013 @default.
- W3094060512 cites W2946805489 @default.
- W3094060512 cites W2948032685 @default.
- W3094060512 cites W2952801176 @default.
- W3094060512 cites W2960308216 @default.
- W3094060512 cites W2963150697 @default.
- W3094060512 cites W2963238715 @default.
- W3094060512 cites W2964317695 @default.
- W3094060512 cites W2968747450 @default.
- W3094060512 cites W2969799848 @default.
- W3094060512 cites W2972018794 @default.
- W3094060512 cites W2978075633 @default.
- W3094060512 cites W2988403248 @default.
- W3094060512 cites W2990768856 @default.
- W3094060512 cites W2992197405 @default.
- W3094060512 cites W2999978966 @default.
- W3094060512 cites W3005715108 @default.
- W3094060512 cites W3007050866 @default.
- W3094060512 cites W3008599372 @default.
- W3094060512 cites W3017125987 @default.
- W3094060512 cites W3017405442 @default.
- W3094060512 cites W3025884834 @default.
- W3094060512 cites W3082326062 @default.
- W3094060512 doi "https://doi.org/10.1109/jsen.2020.3032897" @default.
- W3094060512 hasPublicationYear "2021" @default.
- W3094060512 type Work @default.
- W3094060512 sameAs 3094060512 @default.
- W3094060512 citedByCount "16" @default.
- W3094060512 countsByYear W30940605122021 @default.
- W3094060512 countsByYear W30940605122022 @default.
- W3094060512 countsByYear W30940605122023 @default.
- W3094060512 crossrefType "journal-article" @default.
- W3094060512 hasAuthorship W3094060512A5012656806 @default.
- W3094060512 hasAuthorship W3094060512A5043595651 @default.
- W3094060512 hasAuthorship W3094060512A5045093520 @default.
- W3094060512 hasAuthorship W3094060512A5048140802 @default.
- W3094060512 hasAuthorship W3094060512A5049651824 @default.
- W3094060512 hasAuthorship W3094060512A5059650150 @default.
- W3094060512 hasAuthorship W3094060512A5062266519 @default.
- W3094060512 hasAuthorship W3094060512A5070200818 @default.
- W3094060512 hasAuthorship W3094060512A5073951902 @default.
- W3094060512 hasAuthorship W3094060512A5077950662 @default.
- W3094060512 hasAuthorship W3094060512A5086921316 @default.
- W3094060512 hasConcept C108583219 @default.
- W3094060512 hasConcept C111919701 @default.
- W3094060512 hasConcept C118552586 @default.
- W3094060512 hasConcept C119857082 @default.
- W3094060512 hasConcept C124504099 @default.
- W3094060512 hasConcept C127413603 @default.
- W3094060512 hasConcept C150899416 @default.
- W3094060512 hasConcept C153180895 @default.
- W3094060512 hasConcept C154945302 @default.