AI-Based Early Lung Cancer Diagnostic Framework
dc.contributor.author | Khaled Mohamed Kamal Elborai, Zeina | |
dc.contributor.author | Mostafa Abdelmoniem Mostafa, Elham | |
dc.date.accessioned | 2022-07-24T13:02:33Z | |
dc.date.available | 2022-07-24T13:02:33Z | |
dc.date.issued | 2022 | |
dc.description.abstract | Due to the limitations of human vision, it is simple for radiologists to miss small malignant tumors. At the initial screening, up to 35% of lung nodules are missed. Deep-learning systems understand what a tumor is from real-world instances rather than searching for tumor features that a programmer has predefined in advance. Researchers provide the systems with a huge data set made up of lung CT scans of thousands of individuals, some of whom had cancer and others of whom did. The ability of the computers to identify between lung tumors and benign increases with the number of training scans they have seen. And they perform this task more precisely than earlier, non-AI systems. | en_US |
dc.description.sponsorship | Dr/ Samer Ibrahim Mohamed | en_US |
dc.identifier.citation | Faculty Of Engineering Graduation Project 2020- 2022 | en_US |
dc.identifier.uri | https://2u.pw/LmUBY | |
dc.language.iso | en | en_US |
dc.publisher | MSA | en_US |
dc.relation.ispartofseries | Faculty Of Engineering Graduation Project 2020- 2022; | |
dc.subject | university of modern sciences and arts | en_US |
dc.subject | جامعة أكتوبر للعلوم الحديثة و الأداب | en_US |
dc.subject | MSA university | en_US |
dc.subject | October university for modern sciences and arts | en_US |
dc.subject | Lung Cancer | en_US |
dc.title | AI-Based Early Lung Cancer Diagnostic Framework | en_US |
dc.type | Other | en_US |