แจ้งเอกสารไม่ครบถ้วน, ไม่ตรงกับชื่อเรื่อง หรือมีข้อผิดพลาดเกี่ยวกับเอกสาร ติดต่อที่นี่ ==>
หากไม่มีอีเมลผู้รับให้กรอก thailis-noc@uni.net.th
หากไม่มีอีเมลผู้รับให้กรอก thailis-noc@uni.net.th
Jakkree Srinonchat. VGGNet integration for kidney tumor classification. (). King Mongkut's University of Technology North Bangkok. Central Library. : , 2024.
VGGNet integration for kidney tumor classification
Name: Anuruk Prommakhot
Organization :
Rajamangala University of Technology Thanyaburi. Faculty of Engineering
Email :
anuruk_p@mail.rmutt.ac.th
Name: Jakkree Srinonchat
Organization :
Rajamangala University of Technology Thanyaburi. Faculty of Engineering
Email :
jakkree_s@rmutt.ac.th
keyword:
VGGNet
Abstract:
Deep convolutional neural networks
(DCNNs) have recently become more critical diagnostic
tools. DCNNs have been shown in previous studies to have
performance levels comparable to those of competent medical
professionals, mainly when used in the study of kidney cancers.
It was especially true when DCNNs were used to investigate
kidney tumors. We relied on very complex convolutional
networks, also known as very deep convolutional networks
(VGGNet), to ensure our study's accuracy and improve the
precision of our large-scale image recognition. These networks
are also known as very general-purpose gradient-boosting
networks. This was done to ensure that the results of our
investigation were reliable. After that, these networks were
used to examine the Kidney Tumor dataset, which included
various symptoms associated with the illness. Our research
findings were encouraging because the model demonstrated
astonishingly high accuracy rates of one hundred percent for
categorizing normal, cyst, stone, and tumor, respectively. It
offered a great deal of solace at difficult times. In the realm of
medical diagnostics, a significant new development has just
recently been finished being implemented
King Mongkut's University of Technology North Bangkok. Central Library
Address:
BANGKOK
Email:
library@kmutnb.ac.th
Created:
2024
Modified:
2025-01-23
Issued:
2025-01-23
บทความ/Article
application/pdf
BibliograpyCitation :
In Rajamangala University of Technology Krungthep. 12th International Electrical Engineering Congress (iEECON 2024) (pp.271-275). Bangkok : Rajamangala University of Technology Krungthep
eng
©copyrights King Mongkut's University of Technology North Bangkok
RightsAccess:
ใช้เวลา
0.02721 วินาที
Anuruk Prommakhot
| Title | Contributor | Type |
|---|---|---|
| VGGNet integration for kidney tumor classification
มหาวิทยาลัยเทคโนโลยีพระจอมเกล้าพระนครเหนือ Anuruk Prommakhot;Jakkree Srinonchat | บทความ/Article |
Jakkree Srinonchat
| Title | Contributor | Type |
|---|---|---|
| VGGNet integration for kidney tumor classification
มหาวิทยาลัยเทคโนโลยีพระจอมเกล้าพระนครเหนือ Anuruk Prommakhot;Jakkree Srinonchat | บทความ/Article | |
| A deep learning-based system for detecting defects in printed circuit boards
มหาวิทยาลัยเทคโนโลยีพระจอมเกล้าพระนครเหนือ Aekkarat Suksukont;Jakkrit Onshaunjit;Jakkree Srinonchat | บทความ/Article | |
| Object recognition using glove tactile sensor
มหาวิทยาลัยเทคโนโลยีพระจอมเกล้าพระนครเหนือ Somchai Pohtongkam;Jakkree Srinonchat | บทความ/Article |