Comparisons of Deep Learning Approaches to Detect Lung Cancer through Efficient Computer Based CT-based Screening

Wael Natafji, Daniel Einarson

Research output: Contribution to conferencePaperpeer-review

Abstract

Abstract—The avoidance of mortality in lung cancer is highly dependent on finding defects in the lungs early, in order to initiate effective treatments in time. Most often, lung disorders are diagnosed and treated using chest radiographs and CT scans. Methods based on machine learning can complement human observations and increase precisions of accuracy by mapping an CT image against a trained artificial neural network. The efficiency and accuracy of training such a network, however, depends on the availability of the performance of an underlying computer system, and the quality and size of images. The use of neural network structures with high intrinsic performance is therefore significant. This contribution focuses on comparisons between different Convolutional Neural Networks and formats on datasets to contribute to a good basis for decision-making in the context of possible lung cancer.
Original languageSwedish
Publication statusPublished - 2024
EventInternational Conference on Artificial Intelligence, Computer, Data Sciences and Applications - University of Seychelles, Mahe, Seychelles
Duration: 2024-Feb-01 → …
http://www.acdsa.org/

Conference

ConferenceInternational Conference on Artificial Intelligence, Computer, Data Sciences and Applications
Abbreviated titleACDSA
Country/TerritorySeychelles
CityMahe
Period24-02-01 → …
Internet address

Swedish Standard Keywords

  • Natural sciences (1)

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