Please use this identifier to cite or link to this item:
http://repository.i3l.ac.id/jspui/handle/123456789/1054
Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Lunoto, Dennis | - |
dc.date.accessioned | 2024-05-21T07:24:36Z | - |
dc.date.available | 2024-05-21T07:24:36Z | - |
dc.date.issued | 2024-01-31 | - |
dc.identifier.uri | http://repository.i3l.ac.id/jspui/handle/123456789/1054 | - |
dc.description.abstract | Lung cancer is the most malignant tumor with the highest mortality in the world. Early diagnosis and prognosis are important to improve the patient’s survival rate. With AI, screening which is one of the methods of detection is more sensitive and accurate allowing better analysis. This will in turn assist medical professionals in their line of work and improve the medical field overall. This research will explore a deep learning based on convolutional neural network (CNN), You Only Look Once (Yolo)’s Yolo5, LeNet5, as well as a transfer learning method using four EfficientNet models including EfficientNetB0, EfficientNetB1, EfficientNetB2, and EfficietNetB3. | en_US |
dc.language.iso | en | en_US |
dc.publisher | Indonesia International Institute for Life Sciences | en_US |
dc.relation.ispartofseries | EP BI-002;EP24-040 | - |
dc.subject | CNN | en_US |
dc.subject | Lung cancer | en_US |
dc.title | EfficientNet Based Detection of Lung Cancer Based on Histopathological Image | en_US |
dc.type | Thesis | en_US |
Appears in Collections: | BI |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
004. EP2024_BI_Dennis Lanuto.pdf Restricted Access | Full text | 2.59 MB | Adobe PDF | View/Open Request a copy |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.