Deep-Precognitive Diagnosis: Preventing Future Pandemics by Novel Disease Detection With Biologically-Inspired Conv-Fuzzy Network.
COVID-19
Deep learning
computer-aided diagnosis
medical imaging
pandemics
Journal
IEEE access : practical innovations, open solutions
ISSN: 2169-3536
Titre abrégé: IEEE Access
Pays: United States
ID NLM: 101639462
Informations de publication
Date de publication:
2022
2022
Historique:
entrez:
1
4
2022
pubmed:
2
4
2022
medline:
2
4
2022
Statut:
ppublish
Résumé
Deep learning-based Computer-Aided Diagnosis has gained immense attention in recent years due to its capability to enhance diagnostic performance and elucidate complex clinical tasks. However, conventional supervised deep learning models are incapable of recognizing novel diseases that do not exist in the training dataset. Automated early-stage detection of novel infectious diseases can be vital in controlling their rapid spread. Moreover, the development of a conventional CAD model is only possible after disease outbreaks and datasets become available for training (viz. COVID-19 outbreak). Since novel diseases are unknown and cannot be included in training data, it is challenging to recognize them through existing supervised deep learning models. Even after data becomes available, recognizing new classes with conventional models requires a complete extensive re-training. The present study is the
Identifiants
pubmed: 35360503
doi: 10.1109/access.2022.3153059
pmc: PMC8967064
mid: NIHMS1786284
doi:
Types de publication
Journal Article
Langues
eng
Pagination
23167-23185Subventions
Organisme : NIMH NIH HHS
ID : K01 MH123896
Pays : United States
Organisme : NIGMS NIH HHS
ID : P41 GM103712
Pays : United States
Organisme : NIGMS NIH HHS
ID : R01 GM134020
Pays : United States
Déclaration de conflit d'intérêts
CONFLICTS OF INTEREST The authors hereby declare that there is no conflict of interest.
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