TELKOMNIKA Telecommunication, Computing, Electronics and Control
Lung cancer classification based on CT scan image by applying FCM segmentation and neural network technique
Dublin Core
Title
TELKOMNIKA Telecommunication, Computing, Electronics and Control
Lung cancer classification based on CT scan image by applying FCM segmentation and neural network technique
Lung cancer classification based on CT scan image by applying FCM segmentation and neural network technique
Subject
ANFIS
ELM
FCM
GLCM
KELM
Lung cancer
RNN
ELM
FCM
GLCM
KELM
Lung cancer
RNN
Description
The number of people with lung cancer has reached approximately 2.09
million people worldwide. Out of 9.06 million cases of death, 1.76 million
people die due to lung cancer. Lung cancer can be automatically identified
using a computer-aided diagnosis system (CAD) such as image processing.
The steps taken for early detection are pre-processing feature extraction, and
classification. Pre-processing is carried out in several stages, namely grayscale
images, noise removal, and contrast limited adaptive histogram equalization.
This image feature extracted using gray level co-occurrence matrix (GLCM)
and classified using 2 method of neural network which is feed forward neural
network (FFNN) dan feed backward neural network (FBNN). This research
aims to obtain the best neural network model to classify lung cancer a. Based
on training time and accuracy, the best method of FFNN is kernel extreme
learning machine (KELM), with a training time of 12 seconds and an accuracy
of 93.45%, while the best method of FBNN is Backpropagation with a training
time of 18 minutes 04 seconds and an accuracy of 97.5%.
million people worldwide. Out of 9.06 million cases of death, 1.76 million
people die due to lung cancer. Lung cancer can be automatically identified
using a computer-aided diagnosis system (CAD) such as image processing.
The steps taken for early detection are pre-processing feature extraction, and
classification. Pre-processing is carried out in several stages, namely grayscale
images, noise removal, and contrast limited adaptive histogram equalization.
This image feature extracted using gray level co-occurrence matrix (GLCM)
and classified using 2 method of neural network which is feed forward neural
network (FFNN) dan feed backward neural network (FBNN). This research
aims to obtain the best neural network model to classify lung cancer a. Based
on training time and accuracy, the best method of FFNN is kernel extreme
learning machine (KELM), with a training time of 12 seconds and an accuracy
of 93.45%, while the best method of FBNN is Backpropagation with a training
time of 18 minutes 04 seconds and an accuracy of 97.5%.
Creator
Ahmad Zoebad Foeady, Siti Ria Riqmawatin, Dian Candra Rini Novitasari
Source
http://journal.uad.ac.id/index.php/TELKOMNIKA
Date
Jan 21, 2021
Contributor
peri irawan
Format
pdf
Language
english
Type
text
Files
Collection
Citation
Ahmad Zoebad Foeady, Siti Ria Riqmawatin, Dian Candra Rini Novitasari, “TELKOMNIKA Telecommunication, Computing, Electronics and Control
Lung cancer classification based on CT scan image by applying FCM segmentation and neural network technique,” Repository Horizon University Indonesia, accessed November 22, 2024, https://repository.horizon.ac.id/items/show/4098.
Lung cancer classification based on CT scan image by applying FCM segmentation and neural network technique,” Repository Horizon University Indonesia, accessed November 22, 2024, https://repository.horizon.ac.id/items/show/4098.