Artikel ; Online: A systematic review on deep learning-based automated cancer diagnosis models.
Journal of cellular and molecular medicine
2024 Band 28, Heft 6, Seite(n) e18144
Abstract: Deep learning is gaining importance due to its wide range of applications. Many researchers have utilized deep learning (DL) models for the automated diagnosis of cancer patients. This paper provides a systematic review of DL models for automated ... ...
Abstract | Deep learning is gaining importance due to its wide range of applications. Many researchers have utilized deep learning (DL) models for the automated diagnosis of cancer patients. This paper provides a systematic review of DL models for automated diagnosis of cancer patients. Initially, various DL models for cancer diagnosis are presented. Five major categories of cancers such as breast, lung, liver, brain and cervical cancer are considered. As these categories of cancers have a very high percentage of occurrences with high mortality rate. The comparative analysis of different types of DL models is drawn for the diagnosis of cancer at early stages by considering the latest research articles from 2016 to 2022. After comprehensive comparative analysis, it is found that most of the researchers achieved appreciable accuracy with implementation of the convolutional neural network model. These utilized the pretrained models for automated diagnosis of cancer patients. Various shortcomings with the existing DL-based automated cancer diagnosis models are also been presented. Finally, future directions are discussed to facilitate further research for automated diagnosis of cancer patients. |
|||||
---|---|---|---|---|---|---|
Mesh-Begriff(e) | Humans ; Deep Learning ; Lung ; Neural Networks, Computer ; Tomography, X-Ray Computed ; Neoplasms/diagnosis ; Diagnosis, Computer-Assisted | |||||
Sprache | Englisch | |||||
Erscheinungsdatum | 2024-03-01 | |||||
Erscheinungsland | England | |||||
Dokumenttyp | Journal Article ; Systematic Review | |||||
ZDB-ID | 2074559-X | |||||
ISSN | 1582-4934 ; 1582-4934 ; 1582-1838 | |||||
ISSN (online) | 1582-4934 | |||||
ISSN | 1582-4934 ; 1582-1838 | |||||
DOI | 10.1111/jcmm.18144 | |||||
Signatur |
|
|||||
Datenquelle | MEDical Literature Analysis and Retrieval System OnLINE |
Zusatzmaterialien
Kategorien
Verfügbar in ZB MED Köln/Königswinter
Zs.A 5752: Hefte anzeigen | Standort: Je nach Verfügbarkeit (siehe Angabe bei Bestand) bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular Jg. 1995 - 2021: Lesesall (2.OG) ab Jg. 2022: Lesesaal (EG) |
Über subito bestellen
Dieser Service ist kostenpflichtig (siehe Lieferbedingungen von subito). Bestellungen, die einen Artikel nebst Supplementary Material umfassen, werden grundsätzlich wie mehrfache Bestellungen bearbeitet. Gebühren fallen in diesen Fällen für jede einzelne Bestellung an.